1
|
Ocaña-Tienda B, Pérez-García VM. Mathematical modeling of brain metastases growth and response to therapies: A review. Math Biosci 2024; 373:109207. [PMID: 38759950 DOI: 10.1016/j.mbs.2024.109207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 04/04/2024] [Accepted: 05/10/2024] [Indexed: 05/19/2024]
Abstract
Brain metastases (BMs) are the most common intracranial tumor type and a significant health concern, affecting approximately 10% to 30% of all oncological patients. Although significant progress is being made, many aspects of the metastatic process to the brain and the growth of the resulting lesions are still not well understood. There is a need for an improved understanding of the growth dynamics and the response to treatment of these tumors. Mathematical models have been proven valuable for drawing inferences and making predictions in different fields of cancer research, but few mathematical works have considered BMs. This comprehensive review aims to establish a unified platform and contribute to fostering emerging efforts dedicated to enhancing our mathematical understanding of this intricate and challenging disease. We focus on the progress made in the initial stages of mathematical modeling research regarding BMs and the significant insights gained from such studies. We also explore the vital role of mathematical modeling in predicting treatment outcomes and enhancing the quality of clinical decision-making for patients facing BMs.
Collapse
Affiliation(s)
- Beatriz Ocaña-Tienda
- Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Avda. Camilo José Cela s/n, 13071, Ciudad Real, Spain.
| | - Víctor M Pérez-García
- Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Avda. Camilo José Cela s/n, 13071, Ciudad Real, Spain.
| |
Collapse
|
2
|
Lorenzo G, Heiselman JS, Liss MA, Miga MI, Gomez H, Yankeelov TE, Reali A, Hughes TJ. A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model. CANCER RESEARCH COMMUNICATIONS 2024; 4:617-633. [PMID: 38426815 PMCID: PMC10906139 DOI: 10.1158/2767-9764.crc-23-0449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/15/2023] [Accepted: 02/09/2024] [Indexed: 03/02/2024]
Abstract
Active surveillance (AS) is a suitable management option for newly diagnosed prostate cancer, which usually presents low to intermediate clinical risk. Patients enrolled in AS have their tumor monitored via longitudinal multiparametric MRI (mpMRI), PSA tests, and biopsies. Hence, treatment is prescribed when these tests identify progression to higher-risk prostate cancer. However, current AS protocols rely on detecting tumor progression through direct observation according to population-based monitoring strategies. This approach limits the design of patient-specific AS plans and may delay the detection of tumor progression. Here, we present a pilot study to address these issues by leveraging personalized computational predictions of prostate cancer growth. Our forecasts are obtained with a spatiotemporal biomechanistic model informed by patient-specific longitudinal mpMRI data (T2-weighted MRI and apparent diffusion coefficient maps from diffusion-weighted MRI). Our results show that our technology can represent and forecast the global tumor burden for individual patients, achieving concordance correlation coefficients from 0.93 to 0.99 across our cohort (n = 7). In addition, we identify a model-based biomarker of higher-risk prostate cancer: the mean proliferation activity of the tumor (P = 0.041). Using logistic regression, we construct a prostate cancer risk classifier based on this biomarker that achieves an area under the ROC curve of 0.83. We further show that coupling our tumor forecasts with this prostate cancer risk classifier enables the early identification of prostate cancer progression to higher-risk disease by more than 1 year. Thus, we posit that our predictive technology constitutes a promising clinical decision-making tool to design personalized AS plans for patients with prostate cancer. SIGNIFICANCE Personalization of a biomechanistic model of prostate cancer with mpMRI data enables the prediction of tumor progression, thereby showing promise to guide clinical decision-making during AS for each individual patient.
Collapse
Affiliation(s)
- Guillermo Lorenzo
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas
| | - Jon S. Heiselman
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
- Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Michael A. Liss
- Department of Urology, The University of Texas Health Science Center at San Antonio, San Antonio, Texas
| | - Michael I. Miga
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
- Vanderbilt Institute for Surgery and Engineering, Vanderbilt University, Nashville, Tennessee
- Department of Neurological Surgery, Radiology, and Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Hector Gomez
- School of Mechanical Engineering, Weldon School of Biomedical Engineering, and Purdue Institute for Cancer Research, Purdue University, West Lafayette, Indiana
| | - Thomas E. Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas
- Livestrong Cancer Institutes and Departments of Biomedical Engineering, Diagnostic Medicine, and Oncology, The University of Texas at Austin, Austin, Texas
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Alessandro Reali
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
| | - Thomas J.R. Hughes
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas
| |
Collapse
|
3
|
Yankeelov TE, Hormuth DA, Lima EA, Lorenzo G, Wu C, Okereke LC, Rauch GM, Venkatesan AM, Chung C. Designing clinical trials for patients who are not average. iScience 2024; 27:108589. [PMID: 38169893 PMCID: PMC10758956 DOI: 10.1016/j.isci.2023.108589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2024] Open
Abstract
The heterogeneity inherent in cancer means that even a successful clinical trial merely results in a therapeutic regimen that achieves, on average, a positive result only in a subset of patients. The only way to optimize an intervention for an individual patient is to reframe their treatment as their own, personalized trial. Toward this goal, we formulate a computational framework for performing personalized trials that rely on four mathematical techniques. First, mathematical models that can be calibrated with patient-specific data to make accurate predictions of response. Second, digital twins built on these models capable of simulating the effects of interventions. Third, optimal control theory applied to the digital twins to optimize outcomes. Fourth, data assimilation to continually update and refine predictions in response to therapeutic interventions. In this perspective, we describe each of these techniques, quantify their "state of readiness", and identify use cases for personalized clinical trials.
Collapse
Affiliation(s)
- Thomas E. Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
- Division of Diagnostic Imaging, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA
| | - David A. Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
| | - Ernesto A.B.F. Lima
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Texas Advanced Computer Center, The University of Texas at Austin, Austin, TX 78712, USA
| | - Guillermo Lorenzo
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Civil Engineering and Architecture, University of Pavia, 27100 Pavia, Italy
| | - Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Lois C. Okereke
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Gaiane M. Rauch
- Department of Abdominal Imaging, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA
| | - Aradhana M. Venkatesan
- Department of Abdominal Imaging, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA
| |
Collapse
|
4
|
Chu C, Low YLC, Ma L, Wang Y, Cox T, Doré V, Masters CL, Goudey B, Jin L, Pan Y. How Can We Use Mathematical Modeling of Amyloid-β in Alzheimer's Disease Research and Clinical Practices? J Alzheimers Dis 2024; 97:89-100. [PMID: 38007665 DOI: 10.3233/jad-230938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2023]
Abstract
The accumulation of amyloid-β (Aβ) plaques in the brain is considered a hallmark of Alzheimer's disease (AD). Mathematical modeling, capable of predicting the motion and accumulation of Aβ, has obtained increasing interest as a potential alternative to aid the diagnosis of AD and predict disease prognosis. These mathematical models have provided insights into the pathogenesis and progression of AD that are difficult to obtain through experimental studies alone. Mathematical modeling can also simulate the effects of therapeutics on brain Aβ levels, thereby holding potential for drug efficacy simulation and the optimization of personalized treatment approaches. In this review, we provide an overview of the mathematical models that have been used to simulate brain levels of Aβ (oligomers, protofibrils, and/or plaques). We classify the models into five categories: the general ordinary differential equation models, the general partial differential equation models, the network models, the linear optimal ordinary differential equation models, and the modified partial differential equation models (i.e., Smoluchowski equation models). The assumptions, advantages and limitations of these models are discussed. Given the popularity of using the Smoluchowski equation models to simulate brain levels of Aβ, our review summarizes the history and major advancements in these models (e.g., their application to predict the onset of AD and their combined use with network models). This review is intended to bring mathematical modeling to the attention of more scientists and clinical researchers working on AD to promote cross-disciplinary research.
Collapse
Affiliation(s)
- Chenyin Chu
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Yi Ling Clare Low
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Liwei Ma
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Yihan Wang
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Timothy Cox
- The Australian e-Health Research Centre, CSIRO, Parkville, Victoria, Australia
| | - Vincent Doré
- The Australian e-Health Research Centre, CSIRO, Parkville, Victoria, Australia
| | - Colin L Masters
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Benjamin Goudey
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, Victoria, Australia
| | - Liang Jin
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
- Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
| | - Yijun Pan
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
- Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
- Department of Organ Anatomy, Graduate School of Medicine, Tohoku University, Sendai, Miyagi, Japan
| |
Collapse
|
5
|
Nowicka Z, Rentzeperis F, Beck R, Tagal V, Pinto AF, Scanu E, Veith T, Cole J, Ilter D, Viqueira WD, Teer JK, Maksin K, Pasetto S, Abdalah MA, Fiandaca G, Prabhakaran S, Schultz A, Ojwang M, Barnholtz-Sloan JS, Farinhas JM, Gomes AP, Katira P, Andor N. Interactions between ploidy and resource availability shape clonal interference at initiation and recurrence of glioblastoma. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.17.562670. [PMID: 37905142 PMCID: PMC10614845 DOI: 10.1101/2023.10.17.562670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Glioblastoma (GBM) is the most aggressive form of primary brain tumor. Complete surgical resection of GBM is almost impossible due to the infiltrative nature of the cancer. While no evidence for recent selection events have been found after diagnosis, the selective forces that govern gliomagenesis are strong, shaping the tumor's cell composition during the initial progression to malignancy with late consequences for invasiveness and therapy response. We present a mathematical model that simulates the growth and invasion of a glioma, given its ploidy level and the nature of its brain tissue micro-environment (TME), and use it to make inferences about GBM initiation and response to standard-of-care treatment. We approximate the spatial distribution of resource access in the TME through integration of in-silico modelling, multi-omics data and image analysis of primary and recurrent GBM. In the pre-malignant setting, our in-silico results suggest that low ploidy cancer cells are more resistant to starvation-induced cell death. In the malignant setting, between first and second surgery, simulated tumors with different ploidy compositions progressed at different rates. Whether higher ploidy predicted fast recurrence, however, depended on the TME. Historical data supports this dependence on TME resources, as shown by a significant correlation between the median glucose uptake rates in human tissues and the median ploidy of cancer types that arise in the respective tissues (Spearman r = -0.70; P = 0.026). Taken together our findings suggest that availability of metabolic substrates in the TME drives different cell fate decisions for cancer cells with different ploidy and shapes GBM disease initiation and relapse characteristics.
Collapse
Affiliation(s)
- Zuzanna Nowicka
- Department of Biostatistics and Translational Medicine, Medical University of Łódź, Łódź, Poland
| | | | - Richard Beck
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Vural Tagal
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Ana Forero Pinto
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Elisa Scanu
- Queen Mary University of London, London, United Kingdom
| | - Thomas Veith
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA
- Cancer Biology PhD Program, University of South Florida, Tampa, FL, USA
| | - Jackson Cole
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Didem Ilter
- Department of Molecular Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | | | - Jamie K. Teer
- Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | | | - Stefano Pasetto
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | | | - Giada Fiandaca
- Department of Cellular, Computational and Integrative Biology, University of Trento, Tento, Italy
| | - Sandhya Prabhakaran
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Andrew Schultz
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Maureiq Ojwang
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Jill S. Barnholtz-Sloan
- Center for Biomedical Informatics & Information Technology and Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | | | - Ana P. Gomes
- Department of Molecular Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Parag Katira
- Department of Mechanical Engineering, San Diego State University, San Diego, CA, USA
| | - Noemi Andor
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA
| |
Collapse
|
6
|
Bond KM, Curtin L, Ranjbar S, Afshari AE, Hu LS, Rubin JB, Swanson KR. An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients. Front Oncol 2023; 13:1185738. [PMID: 37849813 PMCID: PMC10578440 DOI: 10.3389/fonc.2023.1185738] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 08/21/2023] [Indexed: 10/19/2023] Open
Abstract
Imaging is central to the clinical surveillance of brain tumors yet it provides limited insight into a tumor's underlying biology. Machine learning and other mathematical modeling approaches can leverage paired magnetic resonance images and image-localized tissue samples to predict almost any characteristic of a tumor. Image-based modeling takes advantage of the spatial resolution of routine clinical scans and can be applied to measure biological differences within a tumor, changes over time, as well as the variance between patients. This approach is non-invasive and circumvents the intrinsic challenges of inter- and intratumoral heterogeneity that have historically hindered the complete assessment of tumor biology and treatment responsiveness. It can also reveal tumor characteristics that may guide both surgical and medical decision-making in real-time. Here we describe a general framework for the acquisition of image-localized biopsies and the construction of spatiotemporal radiomics models, as well as case examples of how this approach may be used to address clinically relevant questions.
Collapse
Affiliation(s)
- Kamila M. Bond
- Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, United States
- Hospital of University of Pennsylvania, Department of Neurosurgery, Philadelphia, PA, United States
| | - Lee Curtin
- Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, United States
| | - Sara Ranjbar
- Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, United States
| | - Ariana E. Afshari
- Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, United States
| | - Leland S. Hu
- Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, United States
- Department of Radiology, Mayo Clinic, Phoenix, AZ, United States
| | - Joshua B. Rubin
- Departments of Neuroscience and Pediatrics, Washington University School of Medicine, St. Louis, MO, United States
| | - Kristin R. Swanson
- Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, United States
| |
Collapse
|
7
|
Jørgensen ACS, Hill CS, Sturrock M, Tang W, Karamched SR, Gorup D, Lythgoe MF, Parrinello S, Marguerat S, Shahrezaei V. Data-driven spatio-temporal modelling of glioblastoma. ROYAL SOCIETY OPEN SCIENCE 2023; 10:221444. [PMID: 36968241 PMCID: PMC10031411 DOI: 10.1098/rsos.221444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
Mathematical oncology provides unique and invaluable insights into tumour growth on both the microscopic and macroscopic levels. This review presents state-of-the-art modelling techniques and focuses on their role in understanding glioblastoma, a malignant form of brain cancer. For each approach, we summarize the scope, drawbacks and assets. We highlight the potential clinical applications of each modelling technique and discuss the connections between the mathematical models and the molecular and imaging data used to inform them. By doing so, we aim to prime cancer researchers with current and emerging computational tools for understanding tumour progression. By providing an in-depth picture of the different modelling techniques, we also aim to assist researchers who seek to build and develop their own models and the associated inference frameworks. Our article thus strikes a unique balance. On the one hand, we provide a comprehensive overview of the available modelling techniques and their applications, including key mathematical expressions. On the other hand, the content is accessible to mathematicians and biomedical scientists alike to accommodate the interdisciplinary nature of cancer research.
Collapse
Affiliation(s)
| | - Ciaran Scott Hill
- Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
- Samantha Dickson Brain Cancer Unit, UCL Cancer Institute, London WC1E 6DD, UK
| | - Marc Sturrock
- Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, Dublin D02 YN77, Ireland
| | - Wenhao Tang
- Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London SW7 2AZ, UK
| | - Saketh R. Karamched
- Division of Medicine, Centre for Advanced Biomedical Imaging, University College London (UCL), London WC1E 6BT, UK
| | - Dunja Gorup
- Division of Medicine, Centre for Advanced Biomedical Imaging, University College London (UCL), London WC1E 6BT, UK
| | - Mark F. Lythgoe
- Division of Medicine, Centre for Advanced Biomedical Imaging, University College London (UCL), London WC1E 6BT, UK
| | - Simona Parrinello
- Samantha Dickson Brain Cancer Unit, UCL Cancer Institute, London WC1E 6DD, UK
| | - Samuel Marguerat
- Genomics Translational Technology Platform, UCL Cancer Institute, University College London, London WC1E 6DD, UK
| | - Vahid Shahrezaei
- Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London SW7 2AZ, UK
| |
Collapse
|
8
|
Paeoniflorin Regulates NEDD4L/STAT3 Pathway to Induce Ferroptosis in Human Glioma Cells. JOURNAL OF ONCOLOGY 2022; 2022:6093216. [PMID: 36618071 PMCID: PMC9812627 DOI: 10.1155/2022/6093216] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 06/04/2022] [Accepted: 12/10/2022] [Indexed: 12/29/2022]
Abstract
Background Paeoniflorin is an active component of a widely used traditional Chinese medicine with antitumor activity through ferroptosis induction. It has been reported recently that ferroptosis is emerging in certain types of cancer; however, its relevance in glioma is still not well studied. Methods CCK8 assay was performed for cell proliferation. Expression of mRNA and protein was tested by qPCR and western blot, respectively. Clinical section samples were detected by IHC. The relationship between NEDD4L and STAT3 was validated by a coimmunoprecipitation assay. Apoptosis was identified by TUNEL assay. A xenograft mouse model was utilized to validate the potential of paeoniflorin toward glioma cancer cells. Results The data suggested that paeoniflorin could increase NEDD4L expression in glioma cells. The NEDD4L expression level was lower in glioma cancer tissues compared to adjacent normal tissues, and it correlates with poor prognosis. Meanwhile, NEDD4L mediates the ubiquitination of STAT3. Furthermore, increased NEDD4L significantly inhibited cell viability and induced accumulation of intracellular ROS levels, accompanied by decreased expression of key ferroptosis factors Nrl2 and GPX4, while NEDD4L knockdown had a reverse effect, suggesting that ferroptosis could be involved. NEDD4L-induced ferroptosis could be rescued by forced expression of STAT3. A xenograft nude mouse model showed that paeoniflorin inhibits tumor growth and further sensitizes glioma cells to RSL3, another well-known ferroptosis inducer. Conclusions In summary, this study demonstrated that paeoniflorin might function as an effective drug for glioma by inducing ferroptosis via upregulation of NEDD4L and repression of Nrl2, GPX4, and STAT3.
Collapse
|
9
|
Lorenzo G, di Muzio N, Deantoni CL, Cozzarini C, Fodor A, Briganti A, Montorsi F, Pérez-García VM, Gomez H, Reali A. Patient-specific forecasting of postradiotherapy prostate-specific antigen kinetics enables early prediction of biochemical relapse. iScience 2022; 25:105430. [DOI: 10.1016/j.isci.2022.105430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 09/04/2022] [Accepted: 10/19/2022] [Indexed: 11/06/2022] Open
|
10
|
Hormuth DA, Farhat M, Christenson C, Curl B, Chad Quarles C, Chung C, Yankeelov TE. Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy. Adv Drug Deliv Rev 2022; 187:114367. [PMID: 35654212 PMCID: PMC11165420 DOI: 10.1016/j.addr.2022.114367] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/25/2022] [Accepted: 05/25/2022] [Indexed: 11/01/2022]
Abstract
Immunotherapy has become a fourth pillar in the treatment of brain tumors and, when combined with radiation therapy, may improve patient outcomes and reduce the neurotoxicity. As with other combination therapies, the identification of a treatment schedule that maximizes the synergistic effect of radiation- and immune-therapy is a fundamental challenge. Mechanism-based mathematical modeling is one promising approach to systematically investigate therapeutic combinations to maximize positive outcomes within a rigorous framework. However, successful clinical translation of model-generated combinations of treatment requires patient-specific data to allow the models to be meaningfully initialized and parameterized. Quantitative imaging techniques have emerged as a promising source of high quality, spatially and temporally resolved data for the development and validation of mathematical models. In this review, we will present approaches to personalize mechanism-based modeling frameworks with patient data, and then discuss how these techniques could be leveraged to improve brain cancer outcomes through patient-specific modeling and optimization of treatment strategies.
Collapse
Affiliation(s)
- David A Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Maguy Farhat
- Departments of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77230, USA
| | - Chase Christenson
- Departments of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
| | - Brandon Curl
- Departments of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77230, USA
| | - C Chad Quarles
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Caroline Chung
- Departments of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77230, USA
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Oncology, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Imaging Physics, MD Anderson Cancer Center, Houston, TX 77230, USA
| |
Collapse
|
11
|
Ebrahimi Zade A, Shahabi Haghighi S, Soltani M. Deep Neural Networks for Neuro-oncology: Towards Patient Individualized Design of Chemo-Radiation Therapy for Glioblastoma Patients. J Biomed Inform 2022; 127:104006. [DOI: 10.1016/j.jbi.2022.104006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 01/04/2022] [Accepted: 01/25/2022] [Indexed: 11/28/2022]
|
12
|
Atorvastatin-mediated rescue of cancer-related cognitive changes in combined anticancer therapies. PLoS Comput Biol 2021; 17:e1009457. [PMID: 34669701 PMCID: PMC8559965 DOI: 10.1371/journal.pcbi.1009457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 11/01/2021] [Accepted: 09/17/2021] [Indexed: 11/19/2022] Open
Abstract
Acute administration of trastuzumab (TZB) may induce various forms of cognitive impairment. These cancer-related cognitive changes (CRCC) are regulated by an adverse biological process involving cancer stem cells (CSCs) and IL-6. Recent studies have reported that atorvastatin (ATV) may change the dynamic of cognitive impairment in a combination (TZB+ATV) therapy. In this study, we investigate the mutual interactions between cancer stem cells and the tumor cells that facilitate cognitive impairment during long term TZB therapy by developing a mathematical model that involves IL-6 and the key apoptotic regulation. These include the densities of tumor cells and CSCs, and the concentrations of intracellular signaling molecules (NFκB, Bcl-2, BAX). We apply the mathematical model to a single or combination (ATV+TZB) therapy used in the experiments to demonstrate that the CSCs can enhance CRCC by secreting IL-6 and ATV may interfere the whole regulation. We show that the model can both reproduce the major experimental observation on onset and prevention of CRCC, and suggest several important predictions to guide future experiments with the goal of the development of new anti-tumor and anti-CRCC strategies. Moreover, using this model, we investigate the fundamental mechanism of onset of cognitive impairment in TZB-treated patients and the impact of alternating therapies on the anti-tumor efficacy and intracellular response to different treatment schedules. A conventional drug, trastuzumab (TZB), was shown to be an effective weapon in killing cancer cells in brain. However, long term treatment of TZB increases the proportion of cancer stem cells (CSCs) in the tumour microenvironment (TME) and induces up-regulation of pro-tumoral molecules such as IL-6 in TME. These cancer cells then become more resistant to this chemotherapy through the IL-mediated up-regulation of NFκB and CSCs. More importantly, these changes in TME result in a serious side effect, cognitive impairment called cancer-related cognitive changes (CRCC). The detailed mechanism of CRCC is still poorly understood. However, cancer patients with chemotherapy-induced cognitive impairment can have long-term or delayed mental changes. In this study, we investigated the fundamental mechanism of CRCC in cancer patients based on experiments and a mathematical model that describes how tumor cells interact with CSCs in response to chemo drugs. In particular, we investigate how TZB-induced CSCs with modified IL-6 landscapes shape the cognitive functions in cancer patients. We showed that the combination treatment with another drug, atorvastatin (ATV), can abrogate the TZB-induced CRCC and enhance the survival probability of cancer patients by synergistic anti-tumor effect. We demonstrate that the cognitive functions and survival rates in cancer patients depend on the apoptotic signaling pathways via the critical communication and IL-6 landscapes of stimulated CTCs.
Collapse
|
13
|
Lee J, Lee D, Kim Y. Mathematical model of STAT signalling pathways in cancer development and optimal control approaches. ROYAL SOCIETY OPEN SCIENCE 2021; 8:210594. [PMID: 34631119 PMCID: PMC8479343 DOI: 10.1098/rsos.210594] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 09/03/2021] [Indexed: 06/10/2023]
Abstract
In various diseases, the STAT family display various cellular controls over various challenges faced by the immune system and cell death programs. In this study, we investigate how an intracellular signalling network (STAT1, STAT3, Bcl-2 and BAX) regulates important cellular states, either anti-apoptosis or apoptosis of cancer cells. We adapt a mathematical framework to illustrate how the signalling network can generate a bi-stability condition so that it will induce either apoptosis or anti-apoptosis status of tumour cells. Then, we use this model to develop several anti-tumour strategies including IFN-β infusion. The roles of JAK-STATs signalling in regulation of the cell death program in cancer cells and tumour growth are poorly understood. The mathematical model unveils the structure and functions of the intracellular signalling and cellular outcomes of the anti-tumour drugs in the presence of IFN-β and JAK stimuli. We identify the best injection order of IFN-β and DDP among many possible combinations, which may suggest better infusion strategies of multiple anti-cancer agents at clinics. We finally use an optimal control theory in order to maximize anti-tumour efficacy and minimize administrative costs. In particular, we minimize tumour volume and maximize the apoptotic potential by minimizing the Bcl-2 concentration and maximizing the BAX level while minimizing total injection amount of both IFN-β and JAK2 inhibitors (DDP).
Collapse
Affiliation(s)
- Jonggul Lee
- Pierre Louis Institute of Epidemiology and Public Health, Paris 75012, France
| | - Donggu Lee
- Department of Mathematics, Konkuk University, Seoul 05029, Republic of Korea
| | - Yangjin Kim
- Department of Mathematics, Konkuk University, Seoul 05029, Republic of Korea
- Mathematical Biosciences Institute, Columbus, OH 43210, USA
- Department of Neurosurgery, Harvard Medical School & Brigham and Women’s Hospital, Boston MA 02115, USA
| |
Collapse
|
14
|
Adenis L, Plaszczynski S, Grammaticos B, Pallud J, Badoual M. The Effect of Radiotherapy on Diffuse Low-Grade Gliomas Evolution: Confronting Theory with Clinical Data. J Pers Med 2021; 11:jpm11080818. [PMID: 34442462 PMCID: PMC8401413 DOI: 10.3390/jpm11080818] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/16/2021] [Accepted: 08/18/2021] [Indexed: 12/21/2022] Open
Abstract
Diffuse low-grade gliomas are slowly growing tumors that always recur after treatment. In this paper, we revisit the modeling of the evolution of the tumor radius before and after the radiotherapy process and propose a novel model that is simple yet biologically motivated and that remedies some shortcomings of previously proposed ones. We confront this with clinical data consisting of time series of tumor radii from 43 patient records by using a stochastic optimization technique and obtain very good fits in all cases. Since our model describes the evolution of a tumor from the very first glioma cell, it gives access to the possible age of the tumor. Using the technique of profile likelihood to extract all of the information from the data, we build confidence intervals for the tumor birth age and confirm the fact that low-grade gliomas seem to appear in the late teenage years. Moreover, an approximate analytical expression of the temporal evolution of the tumor radius allows us to explain the correlations observed in the data.
Collapse
Affiliation(s)
- Léo Adenis
- CNRS/IN2P3, IJCLab, Université Paris-Saclay, 91405 Orsay, France; (L.A.); (B.G.); (M.B.)
- IJCLab, Université de Paris, 91405 Orsay, France
| | - Stéphane Plaszczynski
- CNRS/IN2P3, IJCLab, Université Paris-Saclay, 91405 Orsay, France; (L.A.); (B.G.); (M.B.)
- IJCLab, Université de Paris, 91405 Orsay, France
- Correspondence:
| | - Basile Grammaticos
- CNRS/IN2P3, IJCLab, Université Paris-Saclay, 91405 Orsay, France; (L.A.); (B.G.); (M.B.)
- IJCLab, Université de Paris, 91405 Orsay, France
| | - Johan Pallud
- Department of Neurosurgery, GHU Paris, Sainte-Anne Hospital, 75014 Paris, France;
- Université de Paris, Sorbonne Paris Cité, 75014 Paris, France
- Inserm, U1266, IMA-Brain, Institut de Psychiatrie et Neurosciences de Paris, 75014 Paris, France
| | - Mathilde Badoual
- CNRS/IN2P3, IJCLab, Université Paris-Saclay, 91405 Orsay, France; (L.A.); (B.G.); (M.B.)
- IJCLab, Université de Paris, 91405 Orsay, France
| |
Collapse
|
15
|
Hormuth DA, Jarrett AM, Davis T, Yankeelov TE. Towards an Image-Informed Mathematical Model of In Vivo Response to Fractionated Radiation Therapy. Cancers (Basel) 2021; 13:cancers13081765. [PMID: 33917080 PMCID: PMC8067722 DOI: 10.3390/cancers13081765] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 04/01/2021] [Accepted: 04/04/2021] [Indexed: 02/07/2023] Open
Abstract
Simple Summary Using medical imaging data and computational models, we develop a modeling framework to provide personalized treatment response forecasts to fractionated radiation therapy for individual tumors. We evaluate this approach in an animal model of brain cancer and forecast changes in tumor cellularity and vasculature. Abstract Fractionated radiation therapy is central to the treatment of numerous malignancies, including high-grade gliomas where complete surgical resection is often impractical due to its highly invasive nature. Development of approaches to forecast response to fractionated radiation therapy may provide the ability to optimize or adapt treatment plans for radiotherapy. Towards this end, we have developed a family of 18 biologically-based mathematical models describing the response of both tumor and vasculature to fractionated radiation therapy. Importantly, these models can be personalized for individual tumors via quantitative imaging measurements. To evaluate this family of models, rats (n = 7) with U-87 glioblastomas were imaged with magnetic resonance imaging (MRI) before, during, and after treatment with fractionated radiotherapy (with doses of either 2 Gy/day or 4 Gy/day for up to 10 days). Estimates of tumor and blood volume fractions, provided by diffusion-weighted MRI and dynamic contrast-enhanced MRI, respectively, were used to calibrate tumor-specific model parameters. The Akaike Information Criterion was employed to select the most parsimonious model and determine an ensemble averaged model, and the resulting forecasts were evaluated at the global and local level. At the global level, the selected model’s forecast resulted in less than 16.2% error in tumor volume estimates. At the local (voxel) level, the median Pearson correlation coefficient across all prediction time points ranged from 0.57 to 0.87 for all animals. While the ensemble average forecast resulted in increased error (ranging from 4.0% to 1063%) in tumor volume predictions over the selected model, it increased the voxel wise correlation (by greater than 12.3%) for three of the animals. This study demonstrates the feasibility of calibrating a model of response by serial quantitative MRI data collected during fractionated radiotherapy to predict response at the conclusion of treatment.
Collapse
Affiliation(s)
- David A. Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (A.M.J.); (T.E.Y.)
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
- Correspondence:
| | - Angela M. Jarrett
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (A.M.J.); (T.E.Y.)
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
| | - Tessa Davis
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA;
| | - Thomas E. Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (A.M.J.); (T.E.Y.)
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA;
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| |
Collapse
|
16
|
Ebrahimi Zade A, Shahabi Haghighi S, Soltani M. A neuro evolutionary algorithm for patient calibrated prediction of survival in Glioblastoma patients. J Biomed Inform 2021; 115:103694. [PMID: 33545332 DOI: 10.1016/j.jbi.2021.103694] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 01/28/2021] [Accepted: 01/29/2021] [Indexed: 01/30/2023]
Abstract
BACKGROUND AND OBJECTIVES Glioblastoma multiforme (GBM) is the most common and malignant type of primary brain tumors. Radiation therapy (RT) plus concomitant and adjuvant Temozolomide (TMZ) constitute standard treatment of GBM. Existing models for GBM growth do not consider the effect of different schedules on tumor growth and patient survival. However, clinical trials show that treatment schedule and drug dosage significantly affect patient survival. The goal is to provide a patient calibrated model for predicting survival according to the treatment schedule. METHODS We propose a top-down method based on artificial neural networks (ANN) and genetic algorithm (GA) to predict survival of GBM patients. A feed forward undercomplete Autoencoder network is integrated with the neuro-evolutionary (NE) algorithm in order to extract a compressed representation of input clinical data. The proposed NE algorithm uses GA to obtain optimal architecture of a multi-layer perceptron (MLP). Taguchi L16 orthogonal design of experiments is used to tune parameters of the proposed NE algorithm. Finally, the optimal MLP is used to predict survival of GBM patients. RESULTS Data from 8 related clinical trials have been collected and integrated to train the model. From 847 evaluable cases, 719 were used for train and validation and the remaining 128 cases were used to test the model. Mean absolute error of the predictions on the test data is 0.087 months which shows excellent performance of the proposed model in predicting survival of the patients. Also, the results show that the proposed NE algorithm is superior to other existing models in both the mean and variability of the prediction error.
Collapse
Affiliation(s)
- Amir Ebrahimi Zade
- Faculty of Industrial Engineering and Systems Management, Amirkabir University of Technology, Tehran, Iran
| | | | - M Soltani
- Faculty of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran; Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran; Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, Canada; Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada
| |
Collapse
|
17
|
Hormuth DA, Jarrett AM, Lorenzo G, Lima EA, Wu C, Chung C, Patt D, Yankeelov TE. Math, magnets, and medicine: enabling personalized oncology. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2021; 6:79-81. [PMID: 34027102 PMCID: PMC8133535 DOI: 10.1080/23808993.2021.1878023] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 01/15/2021] [Indexed: 02/02/2023]
Affiliation(s)
- David A. Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, United States of America
- Livestrong Cancer Institutes, The University of Texas at Austin, United States of America
| | - Angela M. Jarrett
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, United States of America
- Livestrong Cancer Institutes, The University of Texas at Austin, United States of America
| | - Guillermo Lorenzo
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, United States of America
- Department of Civil Engineering and Architecture, University of Pavia, Via Ferrata 3, 27100 Pavia, Italy
| | - Ernesto A.B.F. Lima
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, United States of America
- Texas Advanced Computing Center, The University of Texas at Austin, United States of America
| | - Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, United States of America
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas at Austin, United States of America
| | - Debra Patt
- Texas Oncology, Austin, Texas, United States of America
| | - Thomas E. Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, United States of America
- Livestrong Cancer Institutes, The University of Texas at Austin, United States of America
- Department of Radiation Oncology, The University of Texas at Austin, United States of America
- Departments of Biomedical Engineering, The University of Texas at Austin, United States of America
- Department of Diagnostic Medicine, The University of Texas at Austin, United States of America
- Department of Oncology, The University of Texas at Austin, Austin, Texas, United States of America
- Departments of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| |
Collapse
|
18
|
Hu LS, Brat DJ, Bloch O, Ramkissoon S, Lesser GJ. The Practical Application of Emerging Technologies Influencing the Diagnosis and Care of Patients With Primary Brain Tumors. Am Soc Clin Oncol Educ Book 2020; 40:1-12. [PMID: 32324425 DOI: 10.1200/edbk_280955] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Over the past decade, a variety of new and innovative technologies has led to important advances in the diagnosis and management of patients with primary malignant brain tumors. New approaches to surgical navigation and tumor localization, advanced imaging to define tumor biology and treatment response, and the widespread adoption of a molecularly defined integrated diagnostic paradigm that complements traditional histopathologic diagnosis continue to impact the day-to-day care of these patients. In the neuro-oncology clinic, discussions with patients about the role of tumor treating fields (TTFields) and the incorporation of next-generation sequencing (NGS) data into therapeutic decision-making are now a standard practice. This article summarizes newer applications of technology influencing the pathologic, neuroimaging, neurosurgical, and medical management of patients with malignant primary brain tumors.
Collapse
Affiliation(s)
- Leland S Hu
- Neuroradiology Section, Department of Radiology, Mayo Clinic, Phoenix, AZ
| | - Daniel J Brat
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Orin Bloch
- Department of Neurologic Surgery, UC Davis Comprehensive Cancer Center, Sacramento, CA
| | - Shakti Ramkissoon
- Foundation Medicine, Inc., Morrisville, NC.,Comprehensive Cancer Center, Wake Forest Baptist Health, Winston-Salem, NC.,Department of Pathology, Wake Forest School of Medicine, Winston-Salem, NC
| | - Glenn J Lesser
- Comprehensive Cancer Center, Wake Forest Baptist Health, Winston-Salem, NC
| |
Collapse
|
19
|
Bayesian Information-Theoretic Calibration of Radiotherapy Sensitivity Parameters for Informing Effective Scanning Protocols in Cancer. J Clin Med 2020; 9:jcm9103208. [PMID: 33027933 PMCID: PMC7601810 DOI: 10.3390/jcm9103208] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Revised: 10/01/2020] [Accepted: 10/03/2020] [Indexed: 12/03/2022] Open
Abstract
With new advancements in technology, it is now possible to collect data for a variety of different metrics describing tumor growth, including tumor volume, composition, and vascularity, among others. For any proposed model of tumor growth and treatment, we observe large variability among individual patients’ parameter values, particularly those relating to treatment response; thus, exploiting the use of these various metrics for model calibration can be helpful to infer such patient-specific parameters both accurately and early, so that treatment protocols can be adjusted mid-course for maximum efficacy. However, taking measurements can be costly and invasive, limiting clinicians to a sparse collection schedule. As such, the determination of optimal times and metrics for which to collect data in order to best inform proper treatment protocols could be of great assistance to clinicians. In this investigation, we employ a Bayesian information-theoretic calibration protocol for experimental design in order to identify the optimal times at which to collect data for informing treatment parameters. Within this procedure, data collection times are chosen sequentially to maximize the reduction in parameter uncertainty with each added measurement, ensuring that a budget of n high-fidelity experimental measurements results in maximum information gain about the low-fidelity model parameter values. In addition to investigating the optimal temporal pattern for data collection, we also develop a framework for deciding which metrics should be utilized at each data collection point. We illustrate this framework with a variety of toy examples, each utilizing a radiotherapy treatment regimen. For each scenario, we analyze the dependence of the predictive power of the low-fidelity model upon the measurement budget.
Collapse
|
20
|
Dehghan M, Narimani N. Radial basis function-generated finite difference scheme for simulating the brain cancer growth model under radiotherapy in various types of computational domains. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 195:105641. [PMID: 32726719 DOI: 10.1016/j.cmpb.2020.105641] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Accepted: 06/28/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVES We extend the original mathematical model, i.e., Swanson's reaction-diffusion equation to the surfaces with no boundary, and we find a new numerical method based on a meshless approach for solving numerically Swanson's reaction-diffusion model in the square and on the sphere. METHODS To solve numerically the Swanson's reaction-diffusion model and its extension version, a collocation meshless technique, namely radial basis function-generated finite difference (RBF-FD) scheme is employed for approximating the spatial variables in the square domain and on the sphere, respectively. Also, to approximate the time variable of the studied models, a first-order semi-implicit backward Euler scheme is used. The resulting fully discrete scheme is a linear system of algebraic equations per time step that is solved via the biconjugate gradient stabilized (BiCGSTAB) iterative algorithm with a zero-fill incomplete lower-upper (ILU) preconditioner. RESULTS The numerical simulations show the growth of untreated and treated brain tumors with radiotherapy using estimated and clinical data (given from magnetic resonance imaging (MRI) scans of patients). Moreover, the results reported here can be used for improving the treatment strategies of the invasive brain tumor. CONCLUSIONS Using the developed numerical scheme in this paper, we can simulate the behavior of the invasive form of brain tumor response to radiotherapy. Also, we can see the effects of radiation response on the brain tumor cell concentration of individual patients. The proposed meshless technique, which is applied for solving numerically the studied model, does not depend on any background mesh or triangulation for approximation in comparison with mesh-dependent methods. Moreover, we apply this technique to the sphere via any set of distributed points easily.
Collapse
Affiliation(s)
- Mehdi Dehghan
- Department of Applied Mathematics, Faculty of Mathematics and Computer Sciences, Amirkabir University of Technology, No. 424, Hafez Ave., Tehran, 15914, Iran.
| | - Niusha Narimani
- Department of Applied Mathematics, Faculty of Mathematics and Computer Sciences, Amirkabir University of Technology, No. 424, Hafez Ave., Tehran, 15914, Iran.
| |
Collapse
|
21
|
Massey SC, Urcuyo JC, Marin BM, Sarkaria JN, Swanson KR. Quantifying Glioblastoma Drug Response Dynamics Incorporating Treatment Sensitivity and Blood Brain Barrier Penetrance From Experimental Data. Front Physiol 2020; 11:830. [PMID: 32973540 PMCID: PMC7472531 DOI: 10.3389/fphys.2020.00830] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Accepted: 06/22/2020] [Indexed: 12/13/2022] Open
Abstract
Many drugs investigated for the treatment of glioblastoma (GBM) have had disappointing clinical trial results. Efficacy of these agents is dependent on adequate delivery to sensitive tumor cell populations, which is limited by the blood-brain barrier (BBB). Additionally, tumor heterogeneity can lead to subpopulations of cells with different sensitivities to anti-cancer drugs, further impacting therapeutic efficacy. Thus, it may be important to evaluate the extent to which BBB limitations and heterogeneous sensitivity each contribute to a drug's failure. To address this challenge, we developed a minimal mathematical model to characterize these elements of overall drug response, informed by time-series bioluminescence imaging data from a treated patient-derived xenograft (PDX) experimental model. By fitting this mathematical model to a preliminary dataset in a series of nonlinear regression steps, we estimated parameter values for individual PDX subjects that correspond to the dynamics seen in experimental data. Using these estimates as a guide for parameter ranges, we ran model simulations and performed a parameter sensitivity analysis using Latin hypercube sampling and partial rank correlation coefficients. Results from this analysis combined with simulations suggest that BBB permeability may play a slightly greater role in therapeutic efficacy than relative drug sensitivity. Additionally, we discuss recommendations for future experiments based on insights gained from this model. Further research in this area will be vital for improving the development of effective new therapies for glioblastoma patients.
Collapse
Affiliation(s)
| | - Javier C. Urcuyo
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ, United States
| | - Bianca Maria Marin
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN, United States
| | - Jann N. Sarkaria
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN, United States
| | - Kristin R. Swanson
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ, United States
- Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, United States
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, United States
| |
Collapse
|
22
|
Rechner LA, Modiri A, Stick LB, Maraldo MV, Aznar MC, Rice SR, Sawant A, Bentzen SM, Vogelius IR, Specht L. Biological optimization for mediastinal lymphoma radiotherapy - a preliminary study. Acta Oncol 2020; 59:879-887. [PMID: 32216586 PMCID: PMC7446040 DOI: 10.1080/0284186x.2020.1733654] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 02/18/2020] [Indexed: 11/30/2022]
Abstract
Purpose: In current radiotherapy (RT) planning and delivery, population-based dose-volume constraints are used to limit the risk of toxicity from incidental irradiation of organs at risks (OARs). However, weighing tradeoffs between target coverage and doses to OARs (or prioritizing different OARs) in a quantitative way for each patient is challenging. We introduce a novel RT planning approach for patients with mediastinal Hodgkin lymphoma (HL) that aims to maximize overall outcome for each patient by optimizing on tumor control and mortality from late effects simultaneously.Material and Methods: We retrospectively analyzed 34 HL patients treated with conformal RT (3DCRT). We used published data to model recurrence and radiation-induced mortality from coronary heart disease and secondary lung and breast cancers. Patient-specific doses to the heart, lung, breast, and target were incorporated in the models as well as age, sex, and cardiac risk factors (CRFs). A preliminary plan of candidate beams was created for each patient in a commercial treatment planning system. From these candidate beams, outcome-optimized (O-OPT) plans for each patient were created with an in-house optimization code that minimized the individual risk of recurrence and mortality from late effects. O-OPT plans were compared to VMAT plans and clinical 3DCRT plans.Results: O-OPT plans generally had the lowest risk, followed by the clinical 3DCRT plans, then the VMAT plans with the highest risk with median (maximum) total risk values of 4.9 (11.1), 5.1 (17.7), and 7.6 (20.3)%, respectively (no CRFs). Compared to clinical 3DCRT plans, O-OPT planning reduced the total risk by at least 1% for 9/34 cases assuming no CRFs and 11/34 cases assuming presence of CRFs.Conclusions: We developed an individualized, outcome-optimized planning technique for HL. Some of the resulting plans were substantially different from clinical plans. The results varied depending on how risk models were defined or prioritized.
Collapse
Affiliation(s)
- Laura Ann Rechner
- Department of Oncology, Section of Radiotherapy, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
- Niels Bohr Institute, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
| | - Arezoo Modiri
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Line Bjerregaard Stick
- Department of Oncology, Section of Radiotherapy, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
- Niels Bohr Institute, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
| | - Maja V. Maraldo
- Department of Oncology, Section of Radiotherapy, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Marianne C. Aznar
- Manchester Cancer Research Centre, Division of Cancer Sciences, The University of Manchester, Manchester, UK
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, UK
| | | | - Amit Sawant
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Søren M. Bentzen
- Greenebaum Comprehensive Cancer Center, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Ivan Richter Vogelius
- Department of Oncology, Section of Radiotherapy, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Lena Specht
- Department of Oncology, Section of Radiotherapy, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| |
Collapse
|
23
|
Hormuth DA, Jarrett AM, Lima EABF, McKenna MT, Fuentes DT, Yankeelov TE. Mechanism-Based Modeling of Tumor Growth and Treatment Response Constrained by Multiparametric Imaging Data. JCO Clin Cancer Inform 2020; 3:1-10. [PMID: 30807209 DOI: 10.1200/cci.18.00055] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Multiparametric imaging is a critical tool in the noninvasive study and assessment of cancer. Imaging methods have evolved over the past several decades to provide quantitative measures of tumor and healthy tissue characteristics related to, for example, cell number, blood volume fraction, blood flow, hypoxia, and metabolism. Mechanistic models of tumor growth also have matured to a point where the incorporation of patient-specific measures could provide clinically relevant predictions of tumor growth and response. In this review, we identify and discuss approaches that use multiparametric imaging data, including diffusion-weighted magnetic resonance imaging, dynamic contrast-enhanced magnetic resonance imaging, diffusion tensor imaging, contrast-enhanced computed tomography, [18F]fluorodeoxyglucose positron emission tomography, and [18F]fluoromisonidazole positron emission tomography to initialize and calibrate mechanistic models of tumor growth and response. We focus the discussion on brain and breast cancers; however, we also identify three emerging areas of application in kidney, pancreatic, and lung cancers. We conclude with a discussion of the future directions for incorporating multiparametric imaging data and mechanistic modeling into clinical decision making for patients with cancer.
Collapse
Affiliation(s)
| | | | | | | | - David T Fuentes
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | | |
Collapse
|
24
|
Storey K, Leder K, Hawkins-Daarud A, Swanson K, Ahmed AU, Rockne RC, Foo J. Glioblastoma Recurrence and the Role of O 6-Methylguanine-DNA Methyltransferase Promoter Methylation. JCO Clin Cancer Inform 2020; 3:1-12. [PMID: 30758983 DOI: 10.1200/cci.18.00062] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
Tumor recurrence in glioblastoma multiforme (GBM) is often attributed to acquired resistance to the standard chemotherapeutic agent, temozolomide (TMZ). Promoter methylation of the DNA repair gene MGMT (O6-methylguanine-DNA methyltransferase) has been associated with sensitivity to TMZ, whereas increased expression of MGMT has been associated with TMZ resistance. Clinical studies have observed a downward shift in MGMT methylation percentage from primary to recurrent stage tumors; however, the evolutionary processes that drive this shift and more generally the emergence and growth of TMZ-resistant tumor subpopulations are still poorly understood. Here, we develop a mathematical model, parameterized using clinical and experimental data, to investigate the role of MGMT methylation in TMZ resistance during the standard treatment regimen for GBM-surgery, chemotherapy, and radiation. We first found that the observed downward shift in MGMT promoter methylation status between detection and recurrence cannot be explained solely by evolutionary selection. Next, our model suggests that TMZ has an inhibitory effect on maintenance methylation of MGMT after cell division. Finally, incorporating this inhibitory effect, we study the optimal number of TMZ doses per adjuvant cycle for patients with GBM with high and low levels of MGMT methylation at diagnosis.
Collapse
Affiliation(s)
- Katie Storey
- University of Minnesota Twin Cities, Minneapolis, MN
| | - Kevin Leder
- University of Minnesota Twin Cities, Minneapolis, MN
| | | | | | - Atique U Ahmed
- Northwestern University Feinberg School of Medicine, Chicago, IL
| | | | - Jasmine Foo
- University of Minnesota Twin Cities, Minneapolis, MN
| |
Collapse
|
25
|
Mang A, Bakas S, Subramanian S, Davatzikos C, Biros G. Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology. Annu Rev Biomed Eng 2020; 22:309-341. [PMID: 32501772 PMCID: PMC7520881 DOI: 10.1146/annurev-bioeng-062117-121105] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Central nervous system (CNS) tumors come with vastly heterogeneous histologic, molecular, and radiographic landscapes, rendering their precise characterization challenging. The rapidly growing fields of biophysical modeling and radiomics have shown promise in better characterizing the molecular, spatial, and temporal heterogeneity of tumors. Integrative analysis of CNS tumors, including clinically acquired multi-parametric magnetic resonance imaging (mpMRI) and the inverse problem of calibrating biophysical models to mpMRI data, assists in identifying macroscopic quantifiable tumor patterns of invasion and proliferation, potentially leading to improved (a) detection/segmentation of tumor subregions and (b) computer-aided diagnostic/prognostic/predictive modeling. This article presents a summary of (a) biophysical growth modeling and simulation,(b) inverse problems for model calibration, (c) these models' integration with imaging workflows, and (d) their application to clinically relevant studies. We anticipate that such quantitative integrative analysis may even be beneficial in a future revision of the World Health Organization (WHO) classification for CNS tumors, ultimately improving patient survival prospects.
Collapse
Affiliation(s)
- Andreas Mang
- Department of Mathematics, University of Houston, Houston, Texas 77204, USA;
| | - Spyridon Bakas
- Department of Mathematics, University of Houston, Houston, Texas 77204, USA;
| | - Shashank Subramanian
- Oden Institute of Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA; ,
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA); Department of Radiology; and Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA; ,
| | - George Biros
- Oden Institute of Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA; ,
| |
Collapse
|
26
|
Whitmire P, Rickertsen CR, Hawkins-Daarud A, Carrasco E, Lorence J, De Leon G, Curtin L, Bayless S, Clark-Swanson K, Peeri NC, Corpuz C, Lewis-de Los Angeles CP, Bendok BR, Gonzalez-Cuyar L, Vora S, Mrugala MM, Hu LS, Wang L, Porter A, Kumthekar P, Johnston SK, Egan KM, Gatenby R, Canoll P, Rubin JB, Swanson KR. Sex-specific impact of patterns of imageable tumor growth on survival of primary glioblastoma patients. BMC Cancer 2020; 20:447. [PMID: 32429869 PMCID: PMC7238585 DOI: 10.1186/s12885-020-06816-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 04/01/2020] [Indexed: 11/19/2022] Open
Abstract
Background Sex is recognized as a significant determinant of outcome among glioblastoma patients, but the relative prognostic importance of glioblastoma features has not been thoroughly explored for sex differences. Methods Combining multi-modal MR images, biomathematical models, and patient clinical information, this investigation assesses which pretreatment variables have a sex-specific impact on the survival of glioblastoma patients (299 males and 195 females). Results Among males, tumor (T1Gd) radius was a predictor of overall survival (HR = 1.027, p = 0.044). Among females, higher tumor cell net invasion rate was a significant detriment to overall survival (HR = 1.011, p < 0.001). Female extreme survivors had significantly smaller tumors (T1Gd) (p = 0.010 t-test), but tumor size was not correlated with female overall survival (p = 0.955 CPH). Both male and female extreme survivors had significantly lower tumor cell net proliferation rates than other patients (M p = 0.004, F p = 0.001, t-test). Conclusion Despite similar distributions of the MR imaging parameters between males and females, there was a sex-specific difference in how these parameters related to outcomes.
Collapse
Affiliation(s)
- Paula Whitmire
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd , SSB 02-700, Phoenix, AZ, 85054, USA.
| | - Cassandra R Rickertsen
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd , SSB 02-700, Phoenix, AZ, 85054, USA
| | - Andrea Hawkins-Daarud
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd , SSB 02-700, Phoenix, AZ, 85054, USA
| | - Eduardo Carrasco
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd , SSB 02-700, Phoenix, AZ, 85054, USA
| | - Julia Lorence
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd , SSB 02-700, Phoenix, AZ, 85054, USA.,School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Gustavo De Leon
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd , SSB 02-700, Phoenix, AZ, 85054, USA
| | - Lee Curtin
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd , SSB 02-700, Phoenix, AZ, 85054, USA.,Centre for Mathematical Medicine and Biology, University of Nottingham, Nottingham, UK
| | - Spencer Bayless
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd , SSB 02-700, Phoenix, AZ, 85054, USA
| | - Kamala Clark-Swanson
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd , SSB 02-700, Phoenix, AZ, 85054, USA
| | - Noah C Peeri
- Cancer Epidemiology Program, Moffitt Cancer Center, Tampa, FL, USA
| | - Christina Corpuz
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | | | - Bernard R Bendok
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd , SSB 02-700, Phoenix, AZ, 85054, USA.,Department of Neurologic Surgery, Mayo Clinic, Phoenix, AZ, USA
| | - Luis Gonzalez-Cuyar
- Department of Pathology, Division of Neuropathology, University of Washington, Seattle, WA, USA
| | - Sujay Vora
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, USA
| | | | - Leland S Hu
- Department of Radiology, Mayo Clinic, Phoenix, AZ, USA
| | - Lei Wang
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Alyx Porter
- Department of Neurology, Mayo Clinic, Phoenix, AZ, USA
| | - Priya Kumthekar
- Department of Neurology, Robert H Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Sandra K Johnston
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd , SSB 02-700, Phoenix, AZ, 85054, USA.,Department of Radiology, University of Washington, Seattle, WA, USA
| | - Kathleen M Egan
- Cancer Epidemiology Program, Moffitt Cancer Center, Tampa, FL, USA
| | - Robert Gatenby
- Cancer Biology and Evolution Program, Moffitt Cancer Center, Tampa, FL, USA
| | - Peter Canoll
- Division of Neuropathology, Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY, USA
| | - Joshua B Rubin
- Department of Pediatrics, Washington University School of Medicine, St Louis, MO, USA
| | - Kristin R Swanson
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd , SSB 02-700, Phoenix, AZ, 85054, USA
| |
Collapse
|
27
|
Optimal Control Theory for Personalized Therapeutic Regimens in Oncology: Background, History, Challenges, and Opportunities. J Clin Med 2020; 9:jcm9051314. [PMID: 32370195 PMCID: PMC7290915 DOI: 10.3390/jcm9051314] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 04/25/2020] [Accepted: 04/28/2020] [Indexed: 12/13/2022] Open
Abstract
Optimal control theory is branch of mathematics that aims to optimize a solution to a dynamical system. While the concept of using optimal control theory to improve treatment regimens in oncology is not novel, many of the early applications of this mathematical technique were not designed to work with routinely available data or produce results that can eventually be translated to the clinical setting. The purpose of this review is to discuss clinically relevant considerations for formulating and solving optimal control problems for treating cancer patients. Our review focuses on two of the most widely used cancer treatments, radiation therapy and systemic therapy, as they naturally lend themselves to optimal control theory as a means to personalize therapeutic plans in a rigorous fashion. To provide context for optimal control theory to address either of these two modalities, we first discuss the major limitations and difficulties oncologists face when considering alternate regimens for their patients. We then provide a brief introduction to optimal control theory before formulating the optimal control problem in the context of radiation and systemic therapy. We also summarize examples from the literature that illustrate these concepts. Finally, we present both challenges and opportunities for dramatically improving patient outcomes via the integration of clinically relevant, patient-specific, mathematical models and optimal control theory.
Collapse
|
28
|
Massey SC, White H, Whitmire P, Doyle T, Johnston SK, Singleton KW, Jackson PR, Hawkins-Daarud A, Bendok BR, Porter AB, Vora S, Sarkaria JN, Hu LS, Mrugala MM, Swanson KR. Image-based metric of invasiveness predicts response to adjuvant temozolomide for primary glioblastoma. PLoS One 2020; 15:e0230492. [PMID: 32218600 PMCID: PMC7100932 DOI: 10.1371/journal.pone.0230492] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Accepted: 03/02/2020] [Indexed: 12/12/2022] Open
Abstract
Background Temozolomide (TMZ) has been the standard-of-care chemotherapy for glioblastoma (GBM) patients for more than a decade. Despite this long time in use, significant questions remain regarding how best to optimize TMZ therapy for individual patients. Understanding the relationship between TMZ response and factors such as number of adjuvant TMZ cycles, patient age, patient sex, and image–based tumor features, might help predict which GBM patients would benefit most from TMZ, particularly for those whose tumors lack O6–methylguanine–DNA methyltransferase (MGMT) promoter methylation. Methods and findings Using a cohort of 90 newly–diagnosed GBM patients treated according to the standard of care, we examined the relationships between several patient and tumor characteristics and volumetric and survival outcomes during adjuvant chemotherapy. Volumetric changes in MR imaging abnormalities during adjuvant therapy were used to assess TMZ response. T1Gd volumetric response is associated with younger patient age, increased number of TMZ cycles, longer time to nadir volume, and decreased tumor invasiveness. Moreover, increased adjuvant TMZ cycles corresponded with improved volumetric response only among more nodular tumors, and this volumetric response was associated with improved survival outcomes. Finally, in a subcohort of patients with known MGMT methylation status, methylated tumors were more diffusely invasive than unmethylated tumors, suggesting the improved response in nodular tumors is not driven by a preponderance of MGMT methylated tumors. Conclusions Our finding that less diffusely invasive tumors are associated with greater volumetric response to TMZ suggests patients with these tumors may benefit from additional adjuvant TMZ cycles, even for those without MGMT methylation.
Collapse
Affiliation(s)
- Susan Christine Massey
- Mathematical NeuroOncology Laboratory, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, Arizona, United States of America
- * E-mail:
| | - Haylye White
- Mathematical NeuroOncology Laboratory, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Paula Whitmire
- Mathematical NeuroOncology Laboratory, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Tatum Doyle
- Mathematical NeuroOncology Laboratory, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, Arizona, United States of America
- College of Literature, Science and the Arts, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Sandra K. Johnston
- Mathematical NeuroOncology Laboratory, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Radiology, University of Washington, Seattle, Washington, United States of America
| | - Kyle W. Singleton
- Mathematical NeuroOncology Laboratory, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Pamela R. Jackson
- Mathematical NeuroOncology Laboratory, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Andrea Hawkins-Daarud
- Mathematical NeuroOncology Laboratory, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Bernard R. Bendok
- Department of Neurologic Surgery, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Otorhinolaryngology (ENT)/Head and Neck Surgery, Mayo Clinic, Phoenix, Arizona, United States of America
- Neurosurgery Simulation and Innovation Laboratory, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Alyx B. Porter
- Department of Neurology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Sujay Vora
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Jann N. Sarkaria
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Leland S. Hu
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Maciej M. Mrugala
- Department of Neurology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kristin R. Swanson
- Mathematical NeuroOncology Laboratory, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurologic Surgery, Mayo Clinic, Phoenix, Arizona, United States of America
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, Arizona, United States of America
| |
Collapse
|
29
|
Hu LS, Hawkins-Daarud A, Wang L, Li J, Swanson KR. Imaging of intratumoral heterogeneity in high-grade glioma. Cancer Lett 2020; 477:97-106. [PMID: 32112907 DOI: 10.1016/j.canlet.2020.02.025] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 02/17/2020] [Accepted: 02/19/2020] [Indexed: 12/19/2022]
Abstract
High-grade glioma (HGG), and particularly Glioblastoma (GBM), can exhibit pronounced intratumoral heterogeneity that confounds clinical diagnosis and management. While conventional contrast-enhanced MRI lacks the capability to resolve this heterogeneity, advanced MRI techniques and PET imaging offer a spectrum of physiologic and biophysical image features to improve the specificity of imaging diagnoses. Published studies have shown how integrating these advanced techniques can help better define histologically distinct targets for surgical and radiation treatment planning, and help evaluate the regional heterogeneity of tumor recurrence and response assessment following standard adjuvant therapy. Application of texture analysis and machine learning (ML) algorithms has also enabled the emerging field of radiogenomics, which can spatially resolve the regional and genetically distinct subpopulations that coexist within a single GBM tumor. This review focuses on the latest advances in neuro-oncologic imaging and their clinical applications for the assessment of intratumoral heterogeneity.
Collapse
Affiliation(s)
- Leland S Hu
- Department of Radiology, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, AZ, 85054, USA.
| | - Andrea Hawkins-Daarud
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd, Support, Services Building Suite 2-700, Phoenix, AZ, 85054, USA.
| | - Lujia Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA.
| | - Jing Li
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA.
| | - Kristin R Swanson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd, Support, Services Building Suite 2-700, Phoenix, AZ, 85054, USA.
| |
Collapse
|
30
|
Montaseri G, Alfonso JCL, Hatzikirou H, Meyer-Hermann M. A minimal modeling framework of radiation and immune system synergy to assist radiotherapy planning. J Theor Biol 2020; 486:110099. [PMID: 31790681 DOI: 10.1016/j.jtbi.2019.110099] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 10/15/2019] [Accepted: 11/28/2019] [Indexed: 02/07/2023]
Abstract
Recent evidence indicates the ability of radiotherapy to induce local and systemic tumor-specific immune responses as a result of immunogenic cell death. However, fractionation regimes routinely used in clinical practice typically ignore the synergy between radiation and the immune system, and instead attempt to completely eradicate tumors by the direct lethal effect of radiation on cancer cells. This paradigm is expected to change in the near future due to the potential benefits of considering radiation-induced antitumor immunity during treatment planning. Towards this goal, we propose a minimal modeling framework based on key aspects of the tumor-immune system interplay to simulate the effects of radiation on tumors and the immunological consequences of radiotherapy. The impacts of tumor-associated vasculature and intratumoral oxygen-mediated heterogeneity on treatment outcomes are ininvestigated. The model provides estimates of the minimum radiation doses required for tumor eradication given a certain number of treatment fractions. Moreover, estimates of treatment duration for disease control given predetermined fractional radiation doses can be also obtained. Although theoretical in nature, this study motivates the development and establishment of immune-based decision-support tools in radiotherapy planning.
Collapse
Affiliation(s)
- Ghazal Montaseri
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany; Centre for Individualised Infection Medicine (CIIM), Hannover, Germany
| | - Juan Carlos López Alfonso
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany; Department of Gastroenterology, Hepatology and Endocrinology, Hannover Medical School, Hannover, Germany.
| | - Haralampos Hatzikirou
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Michael Meyer-Hermann
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany; Centre for Individualised Infection Medicine (CIIM), Hannover, Germany; Institute of Biochemistry, Biotechnology and Bioinformatics, Technische Universität Braunschweig, Germany.
| |
Collapse
|
31
|
Rayfield CA, Grady F, De Leon G, Rockne R, Carrasco E, Jackson P, Vora M, Johnston SK, Hawkins-Daarud A, Clark-Swanson KR, Whitmire S, Gamez ME, Porter A, Hu L, Gonzalez-Cuyar L, Bendok B, Vora S, Swanson KR. Distinct Phenotypic Clusters of Glioblastoma Growth and Response Kinetics Predict Survival. JCO Clin Cancer Inform 2019; 2:1-14. [PMID: 30652553 DOI: 10.1200/cci.17.00080] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
PURPOSE Despite the intra- and intertumoral heterogeneity seen in glioblastoma multiforme (GBM), there is little definitive data on the underlying cause of the differences in patient survivals. Serial imaging assessment of tumor growth allows quantification of tumor growth kinetics (TGK) measured in terms of changes in the velocity of radial expansion seen on imaging. Because a systematic study of this entire TGK phenotype-growth before treatment and during each treatment to recurrence -has never been coordinately studied in GBMs, we sought to identify whether patients cluster into discrete groups on the basis of their TGK. PATIENTS AND METHODS From our multi-institutional database, we identified 48 patients who underwent maximally safe resection followed by radiotherapy with imaging follow-up through the time of recurrence. The patients were then clustered into two groups through a k-means algorithm taking as input only the TGK before and during treatment. RESULTS There was a significant survival difference between the clusters ( P = .003). Paradoxically, patients among the long-lived cluster had significantly larger tumors at diagnosis ( P = .027) and faster growth before treatment ( P = .003) but demonstrated a better response to adjuvant chemotherapy ( P = .048). A predictive model was built to identify which cluster patients would likely fall into on the basis of information that would be available to clinicians immediately after radiotherapy (accuracy, 90.3%). CONCLUSION Dichotomizing the heterogeneity of GBMs into two populations-one faster growing yet more responsive with increased survival and one slower growing yet less responsive with shorter survival-suggests that many patients who receive standard-of-care treatments may get better benefit from select alternative treatments.
Collapse
Affiliation(s)
- Corbin A Rayfield
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Fillan Grady
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Gustavo De Leon
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Russell Rockne
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Eduardo Carrasco
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Pamela Jackson
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Mayur Vora
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Sandra K Johnston
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Andrea Hawkins-Daarud
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Kamala R Clark-Swanson
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Scott Whitmire
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Mauricio E Gamez
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Alyx Porter
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Leland Hu
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Luis Gonzalez-Cuyar
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Bernard Bendok
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Sujay Vora
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Kristin R Swanson
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| |
Collapse
|
32
|
Nourollahi S, Ghate A, Kim M. Optimal modality selection in external beam radiotherapy. MATHEMATICAL MEDICINE AND BIOLOGY-A JOURNAL OF THE IMA 2019; 36:361-380. [PMID: 30192934 DOI: 10.1093/imammb/dqy013] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Revised: 08/07/2018] [Accepted: 08/13/2018] [Indexed: 12/25/2022]
Abstract
The goal in external beam radiotherapy (EBRT) for cancer is to maximize damage to the tumour while limiting toxic effects on the organs-at-risk. EBRT can be delivered via different modalities such as photons, protons and neutrons. The choice of an optimal modality depends on the anatomy of the irradiated area and the relative physical and biological properties of the modalities under consideration. There is no single universally dominant modality. We present the first-ever mathematical formulation of the optimal modality selection problem. We show that this problem can be tackled by solving the Karush-Kuhn-Tucker conditions of optimality, which reduce to an analytically tractable quartic equation. We perform numerical experiments to gain insights into the effect of biological and physical properties on the choice of an optimal modality or combination of modalities.
Collapse
Affiliation(s)
- Sevnaz Nourollahi
- Department of Industrial & Systems Engineering, University of Washington, Seattle, USA
| | - Archis Ghate
- Department of Industrial & Systems Engineering, University of Washington, Seattle, USA
| | - Minsun Kim
- Department of Radiation Oncology, University of Washington, Seattle, USA
| |
Collapse
|
33
|
Lorenzo G, Pérez-García VM, Mariño A, Pérez-Romasanta LA, Reali A, Gomez H. Mechanistic modelling of prostate-specific antigen dynamics shows potential for personalized prediction of radiation therapy outcome. J R Soc Interface 2019; 16:20190195. [PMID: 31409240 DOI: 10.1098/rsif.2019.0195] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
External beam radiation therapy is a widespread treatment for prostate cancer. The ensuing patient follow-up is based on the evolution of the prostate-specific antigen (PSA). Serum levels of PSA decay due to the radiation-induced death of tumour cells and cancer recurrence usually manifest as a rising PSA. The current definition of biochemical relapse requires that PSA reaches nadir and starts increasing, which delays the use of further treatments. Also, these methods do not account for the post-radiation tumour dynamics that may contain early information on cancer recurrence. Here, we develop three mechanistic models of post-radiation PSA evolution. Our models render superior fits of PSA data in a patient cohort and provide a biological justification for the most common empirical formulation of PSA dynamics. We also found three model-based prognostic variables: the proliferation rate of the survival fraction, the ratio of radiation-induced cell death rate to the survival proliferation rate, and the time to PSA nadir since treatment termination. We argue that these markers may enable the early identification of biochemical relapse, which would permit physicians to subsequently adapt patient monitoring to optimize the detection and treatment of cancer recurrence.
Collapse
Affiliation(s)
- Guillermo Lorenzo
- Dipartimento di Ingegneria Civile e Architettura, Università degli Studi di Pavia, Via Ferrata 3, 27100 Pavia, Italy.,Departamento de Matemáticas, Universidade da Coruña, Campus de Elviña s/n, 15071 A Coruña, Spain
| | - Víctor M Pérez-García
- Mathematical Oncology Laboratory, Universidad de Castilla-La Mancha, Edificio Politécnico, Avenida Camilo José Cela 3, 13071 Ciudad Real, Spain
| | - Alfonso Mariño
- Servicio de Oncología Radioterápica, Centro Oncológico de Galicia, Calle Doctor Camilo Veiras 1, 15009 A Coruña, Spain
| | - Luis A Pérez-Romasanta
- Servicio de Oncología Radioterápica, Hospital Universitario de Salamanca, Paseo de San Vicente 58-182, 37007 Salamanca, Spain
| | - Alessandro Reali
- Dipartimento di Ingegneria Civile e Architettura, Università degli Studi di Pavia, Via Ferrata 3, 27100 Pavia, Italy
| | - Hector Gomez
- School of Mechanical Engineering, Purdue University, 585 Purdue Mall, West Lafayette, IN 47907, USA.,Weldon School of Biomedical Engineering, Purdue University, 206 S. Martin Jischke Drive, West Lafayette, IN 47907, USA.,Purdue Center for Cancer Research, Purdue University, 201 S. University Street, West Lafayette, IN 47907, USA
| |
Collapse
|
34
|
Integrating Mathematical Modeling into the Roadmap for Personalized Adaptive Radiation Therapy. Trends Cancer 2019; 5:467-474. [PMID: 31421904 DOI: 10.1016/j.trecan.2019.06.006] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 06/14/2019] [Accepted: 06/21/2019] [Indexed: 11/21/2022]
Abstract
In current radiation oncology practice, treatment protocols are prescribed based on the average outcomes of large clinical trials, with limited personalization and without adaptations of dose or dose fractionation to individual patients based on their individual clinical responses. Predicting tumor responses to radiation and comparing predictions against observed responses offers an opportunity for novel treatment evaluation. These analyses can lead to protocol adaptation aimed at the improvement of patient outcomes with better therapeutic ratios. We foresee the integration of mathematical models into radiation oncology to simulate individual patient tumor growth and predict treatment response as dynamic biomarkers for personalized adaptive radiation therapy (RT).
Collapse
|
35
|
An optimized generic cerebral tumor growth modeling framework by coupling biomechanical and diffusive models with treatment effects. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.04.034] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
|
36
|
Jacobs J, Rockne RC, Hawkins-Daarud AJ, Jackson PR, Johnston SK, Kinahan P, Swanson KR. Improved model prediction of glioma growth utilizing tissue-specific boundary effects. Math Biosci 2019; 312:59-66. [PMID: 31009624 DOI: 10.1016/j.mbs.2019.04.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 05/04/2018] [Accepted: 04/19/2019] [Indexed: 10/27/2022]
Abstract
Kinetic parameter estimates for mathematical models of glioblastoma multiforme (GBM), derived from clinical scans, have been used to predict the occurrence of hypoxia, necrosis, response to radiation therapy, and overall survival. Modeling GBM growth in a cerebral model encounters anatomical boundaries that interfere with model calibration from clinical measurements. METHODS The effect of boundaries is examined on both spherically symmetric and anatomical models of tumor growth. This effect is incorporated into a method that updates kinetic parameters. The efficacy of this method in reproducing clinical image-derived subject data is evaluated. RESULTS Spherically symmetric simulations of tumor growth with simple boundaries behave predictably when in a linear phase of growth. Anatomic simulations of eleven out of twenty subjects demonstrated improved fit to subject data with the new method. When only subjects exhibiting linear growth are considered, eight out of nine subject demonstrate improved fit to the data. CONCLUSION Anatomical boundaries to tumor growth measurably deflect progression and affect estimates of kinetic parameters. The presented method reliably updates kinetic parameters to fit anatomic computational models to clinically derived subject data when those data are in a linear regime.
Collapse
Affiliation(s)
- Joshua Jacobs
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, USA.
| | - Russell C Rockne
- Division of Mathematical Oncology, City of Hope, Duarte, CA, USA
| | | | | | | | - Paul Kinahan
- Department of Radiology, University of Washington, Seattle, WA, USA
| | | |
Collapse
|
37
|
Sunassee ED, Tan D, Ji N, Brady R, Moros EG, Caudell JJ, Yartsev S, Enderling H. Proliferation saturation index in an adaptive Bayesian approach to predict patient-specific radiotherapy responses. Int J Radiat Biol 2019; 95:1421-1426. [PMID: 30831050 DOI: 10.1080/09553002.2019.1589013] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Purpose: Radiotherapy prescription dose and dose fractionation protocols vary little between individual patients having the same tumor grade and stage. To personalize radiotherapy a predictive model is needed to simulate radiation response. Previous modeling attempts with multiple variables and parameters have been shown to yield excellent data fits at the cost of non-identifiability and clinically unrealistic results. Materials and methods: We develop a mathematical model based on a proliferation saturation index (PSI) that is a measurement of pre-treatment tumor volume-to-carrying capacity ratio that modulates intrinsic tumor growth and radiation response rates. In an adaptive Bayesian approach, we utilize an increasing number of data points for individual patients to predict patient-specific responses to subsequent radiation doses. Results: Model analysis shows that using PSI as the only patient-specific parameter, model simulations can fit longitudinal clinical data with high accuracy (R2=0.84). By analyzing tumor response to radiation using daily CT scans early in the treatment, response to the remaining treatment fractions can be predicted after two weeks with high accuracy (c-index = 0.89). Conclusion: The PSI model may be suited to forecast treatment response for individual patients and offers actionable decision points for mid-treatment protocol adaptation. The presented work provides an actionable image-derived biomarker prior to and during therapy to personalize and adapt radiotherapy.
Collapse
Affiliation(s)
- Enakshi D Sunassee
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA
| | - Dean Tan
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA
| | - Nathan Ji
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA
| | - Renee Brady
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA
| | - Eduardo G Moros
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA.,Department of Cancer Physiology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA
| | - Jimmy J Caudell
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA
| | - Slav Yartsev
- London Health Sciences Centre, London Regional Cancer Program , London , ON , Canada
| | - Heiko Enderling
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA.,Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA
| |
Collapse
|
38
|
Hu LS, Yoon H, Eschbacher JM, Baxter LC, Dueck AC, Nespodzany A, Smith KA, Nakaji P, Xu Y, Wang L, Karis JP, Hawkins-Daarud AJ, Singleton KW, Jackson PR, Anderies BJ, Bendok BR, Zimmerman RS, Quarles C, Porter-Umphrey AB, Mrugala MM, Sharma A, Hoxworth JM, Sattur MG, Sanai N, Koulemberis PE, Krishna C, Mitchell JR, Wu T, Tran NL, Swanson KR, Li J. Accurate Patient-Specific Machine Learning Models of Glioblastoma Invasion Using Transfer Learning. AJNR Am J Neuroradiol 2019; 40:418-425. [PMID: 30819771 DOI: 10.3174/ajnr.a5981] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Accepted: 12/13/2018] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE MR imaging-based modeling of tumor cell density can substantially improve targeted treatment of glioblastoma. Unfortunately, interpatient variability limits the predictive ability of many modeling approaches. We present a transfer learning method that generates individualized patient models, grounded in the wealth of population data, while also detecting and adjusting for interpatient variabilities based on each patient's own histologic data. MATERIALS AND METHODS We recruited patients with primary glioblastoma undergoing image-guided biopsies and preoperative imaging, including contrast-enhanced MR imaging, dynamic susceptibility contrast MR imaging, and diffusion tensor imaging. We calculated relative cerebral blood volume from DSC-MR imaging and mean diffusivity and fractional anisotropy from DTI. Following image coregistration, we assessed tumor cell density for each biopsy and identified corresponding localized MR imaging measurements. We then explored a range of univariate and multivariate predictive models of tumor cell density based on MR imaging measurements in a generalized one-model-fits-all approach. We then implemented both univariate and multivariate individualized transfer learning predictive models, which harness the available population-level data but allow individual variability in their predictions. Finally, we compared Pearson correlation coefficients and mean absolute error between the individualized transfer learning and generalized one-model-fits-all models. RESULTS Tumor cell density significantly correlated with relative CBV (r = 0.33, P < .001), and T1-weighted postcontrast (r = 0.36, P < .001) on univariate analysis after correcting for multiple comparisons. With single-variable modeling (using relative CBV), transfer learning increased predictive performance (r = 0.53, mean absolute error = 15.19%) compared with one-model-fits-all (r = 0.27, mean absolute error = 17.79%). With multivariate modeling, transfer learning further improved performance (r = 0.88, mean absolute error = 5.66%) compared with one-model-fits-all (r = 0.39, mean absolute error = 16.55%). CONCLUSIONS Transfer learning significantly improves predictive modeling performance for quantifying tumor cell density in glioblastoma.
Collapse
Affiliation(s)
- L S Hu
- From the Department of Radiology (L.S.H., J.M.H., J.R.M., T.W., J.L.)
| | - H Yoon
- Arizona State University (H.Y., Y.X., L.W., T.W., J.L.), Tempe, Arizona
| | | | | | - A C Dueck
- Department of Biostatistics (A.C.D.), Mayo Clinic in Arizona, Scottsdale, Arizona
| | | | | | - P Nakaji
- Neurosurgery (K.A.S., P.N., N.S.)
| | - Y Xu
- Arizona State University (H.Y., Y.X., L.W., T.W., J.L.), Tempe, Arizona
| | - L Wang
- Arizona State University (H.Y., Y.X., L.W., T.W., J.L.), Tempe, Arizona
| | | | - A J Hawkins-Daarud
- Precision Neurotherapeutics Lab (A.J.H.-D., K.W.S., P.R.J, B.R.B., K.R.S.)
| | - K W Singleton
- Precision Neurotherapeutics Lab (A.J.H.-D., K.W.S., P.R.J, B.R.B., K.R.S.)
| | - P R Jackson
- Precision Neurotherapeutics Lab (A.J.H.-D., K.W.S., P.R.J, B.R.B., K.R.S.)
| | - B J Anderies
- Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - B R Bendok
- Precision Neurotherapeutics Lab (A.J.H.-D., K.W.S., P.R.J, B.R.B., K.R.S.).,Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - R S Zimmerman
- Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - C Quarles
- Neuroimaging Research (C.Q.), Barrow Neurological Institute, Phoenix, Arizona
| | | | - M M Mrugala
- Department of Neuro-Oncology (A.B.P.-U., M.M.M., A.S.)
| | - A Sharma
- Department of Neuro-Oncology (A.B.P.-U., M.M.M., A.S.)
| | - J M Hoxworth
- From the Department of Radiology (L.S.H., J.M.H., J.R.M., T.W., J.L.)
| | - M G Sattur
- Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - N Sanai
- Neurosurgery (K.A.S., P.N., N.S.)
| | - P E Koulemberis
- Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - C Krishna
- Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - J R Mitchell
- From the Department of Radiology (L.S.H., J.M.H., J.R.M., T.W., J.L.).,H. Lee Moffitt Cancer Center and Research Institute (J.R.M.), Tampa, Florida
| | - T Wu
- From the Department of Radiology (L.S.H., J.M.H., J.R.M., T.W., J.L.).,Arizona State University (H.Y., Y.X., L.W., T.W., J.L.), Tempe, Arizona
| | - N L Tran
- Department of Cancer Biology (N.L.T.), Mayo Clinic in Arizona, Phoenix, Arizona
| | - K R Swanson
- Precision Neurotherapeutics Lab (A.J.H.-D., K.W.S., P.R.J, B.R.B., K.R.S.).,Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - J Li
- From the Department of Radiology (L.S.H., J.M.H., J.R.M., T.W., J.L.).,Arizona State University (H.Y., Y.X., L.W., T.W., J.L.), Tempe, Arizona
| |
Collapse
|
39
|
Kim Y, Lee D, Lee J, Lee S, Lawler S. Role of tumor-associated neutrophils in regulation of tumor growth in lung cancer development: A mathematical model. PLoS One 2019; 14:e0211041. [PMID: 30689655 PMCID: PMC6349324 DOI: 10.1371/journal.pone.0211041] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Accepted: 01/07/2019] [Indexed: 02/06/2023] Open
Abstract
Neutrophils display rapid and potent innate immune responses in various diseases. Tumor-associated neutrophils (TANs) however either induce or overcome immunosuppressive functions of the tumor microenvironment through complex tumor-stroma crosstalk. We developed a mathematical model to address the question of how phenotypic alterations between tumor suppressive N1 TANS, and tumor promoting N2 TANs affect nonlinear tumor growth in a complex tumor microenvironment. The model provides a visual display of the complex behavior of populations of TANs and tumors in response to various TGF-β and IFN-β stimuli. In addition, the effect of anti-tumor drug administration is incorporated in the model in an effort to achieve optimal anti-tumor efficacy. The simulation results from the mathematical model were in good agreement with experimental data. We found that the N2-to-N1 ratio (N21R) index is positively correlated with aggressive tumor growth, suggesting that this may be a good prognostic factor. We also found that the antitumor efficacy increases when the relative ratio (Dap) of delayed apoptotic cell death of N1 and N2 TANs is either very small or relatively large, providing a basis for therapeutically targeting prometastatic N2 TANs.
Collapse
Affiliation(s)
- Yangjin Kim
- Department of Mathematics, Konkuk University, Seoul, Republic of Korea
- Mathematical Biosciences Institute, Ohio State University, Columbus, Ohio, United States of America
- * E-mail:
| | - Donggu Lee
- Department of Mathematics, Konkuk University, Seoul, Republic of Korea
| | - Junho Lee
- Department of Mathematics, Konkuk University, Seoul, Republic of Korea
| | - Seongwon Lee
- Division of Mathematical Models, National Institute for Mathematical Sciences, Daejeon, Republic of Korea
| | - Sean Lawler
- Department of neurosurgery, Harvard Medical School & Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| |
Collapse
|
40
|
Jarrett AM, Lima EABF, Hormuth DA, McKenna MT, Feng X, Ekrut DA, Resende ACM, Brock A, Yankeelov TE. Mathematical models of tumor cell proliferation: A review of the literature. Expert Rev Anticancer Ther 2018; 18:1271-1286. [PMID: 30252552 PMCID: PMC6295418 DOI: 10.1080/14737140.2018.1527689] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
INTRODUCTION A defining hallmark of cancer is aberrant cell proliferation. Efforts to understand the generative properties of cancer cells span all biological scales: from genetic deviations and alterations of metabolic pathways to physical stresses due to overcrowding, as well as the effects of therapeutics and the immune system. While these factors have long been studied in the laboratory, mathematical and computational techniques are being increasingly applied to help understand and forecast tumor growth and treatment response. Advantages of mathematical modeling of proliferation include the ability to simulate and predict the spatiotemporal development of tumors across multiple experimental scales. Central to proliferation modeling is the incorporation of available biological data and validation with experimental data. Areas covered: We present an overview of past and current mathematical strategies directed at understanding tumor cell proliferation. We identify areas for mathematical development as motivated by available experimental and clinical evidence, with a particular emphasis on emerging, non-invasive imaging technologies. Expert commentary: The data required to legitimize mathematical models are often difficult or (currently) impossible to obtain. We suggest areas for further investigation to establish mathematical models that more effectively utilize available data to make informed predictions on tumor cell proliferation.
Collapse
Affiliation(s)
- Angela M Jarrett
- a Institute for Computational Engineering and Sciences , The University of Texas at Austin , Austin , USA
- b Livestrong Cancer Institutes , The University of Texas at Austin , Austin , USA
| | - Ernesto A B F Lima
- a Institute for Computational Engineering and Sciences , The University of Texas at Austin , Austin , USA
| | - David A Hormuth
- a Institute for Computational Engineering and Sciences , The University of Texas at Austin , Austin , USA
- b Livestrong Cancer Institutes , The University of Texas at Austin , Austin , USA
| | - Matthew T McKenna
- c Department of Biomedical Engineering , Vanderbilt University , Nashville , USA
| | - Xinzeng Feng
- a Institute for Computational Engineering and Sciences , The University of Texas at Austin , Austin , USA
| | - David A Ekrut
- a Institute for Computational Engineering and Sciences , The University of Texas at Austin , Austin , USA
| | - Anna Claudia M Resende
- a Institute for Computational Engineering and Sciences , The University of Texas at Austin , Austin , USA
- d Department of Computational Modeling , National Laboratory for Scientific Computing , Petrópolis , Brazil
| | - Amy Brock
- b Livestrong Cancer Institutes , The University of Texas at Austin , Austin , USA
- e Department of Biomedical Engineering , The University of Texas at Austin , Austin , USA
| | - Thomas E Yankeelov
- a Institute for Computational Engineering and Sciences , The University of Texas at Austin , Austin , USA
- b Livestrong Cancer Institutes , The University of Texas at Austin , Austin , USA
- e Department of Biomedical Engineering , The University of Texas at Austin , Austin , USA
- f Department of Diagnostic Medicine , The University of Texas at Austin , Austin , USA
- g Department of Oncology , The University of Texas at Austin , Austin , USA
| |
Collapse
|
41
|
Patterns of re-irradiation for recurrent gliomas and validation of a prognostic score. Radiother Oncol 2018; 130:156-163. [PMID: 30446315 DOI: 10.1016/j.radonc.2018.10.034] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Revised: 10/17/2018] [Accepted: 10/29/2018] [Indexed: 12/17/2022]
Abstract
PURPOSE OR OBJECTIVE Re-irradiation is a generally accepted method for salvage treatment in patients with recurrent glioma. However, no standard radiation regimen has been defined. This study aims to compare the efficacy and safety of different treatment regimens and to independently externally validate a recently published reirradiation risk score. MATERIAL AND METHODS We retrospectively analyzed a cohort of patients with recurrent malignant glioma treated with salvage conventionally fractionated (CFRT), hypofractionated (HFRT) or stereotactic radiotherapy (SRT) between 2007 and 2017 at the University Medical Centers in Utrecht and Groningen. RESULTS Of the 121 patients included, 60 patients (50%) underwent CFRT, 22 (18%) HFRT and 39 (32%) SRT. The primary tumor was grade II-III in 52 patients and grade IV in 69 patients with median Overall Survival (mOS) since first surgery of 113 [Interquartile range: 53.2-137] and 39.7 [24.6-64.9] months respectively (p < 0.01). Overall, mOS from the first day of re-irradiation was 9.7 months [6.5-14.6]. No significant difference in mOS was found between the treatment groups. In multivariate analysis, the Karnofsky performance scale ≥70% (p < 0.01), re-irradiation for first recurrence (p = 0.02), longer time interval between RT start dates (p < 0.01) and smaller planning target volume (p < 0.05) were significant favorable prognostic factors. The reirradiation risk score was validated. CONCLUSION In our series, mOS after reirradiation was sufficient to justify use of this modality. Until a reliable treatment decision tool is developed based on larger retrospective research, the decision for re-irradiation schedule should remain personalized and based on a multidisciplinary evaluation of each patient.
Collapse
|
42
|
Jacob J, Durand T, Feuvret L, Mazeron JJ, Delattre JY, Hoang-Xuan K, Psimaras D, Douzane H, Ribeiro M, Capelle L, Carpentier A, Ricard D, Maingon P. Cognitive impairment and morphological changes after radiation therapy in brain tumors: A review. Radiother Oncol 2018; 128:221-228. [PMID: 30041961 DOI: 10.1016/j.radonc.2018.05.027] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Revised: 05/25/2018] [Accepted: 05/28/2018] [Indexed: 11/18/2022]
Abstract
Life expectancy of patients treated for brain tumors has lengthened due to the therapeutic improvements. Cognitive impairment has been described following brain radiotherapy, but the mechanisms leading to this adverse event remain mostly unknown. Technical evolutions aim at enhancing the therapeutic ratio. Sparing of the healthy tissues has been improved using various approaches; however, few dose constraints have been established regarding brain structures associated with cognitive functions. The aims of this literature review are to report the main brain areas involved in cognitive adverse effects induced by radiotherapy as described in literature, to better understand brain radiosensitivity and to describe potential future improvements.
Collapse
Affiliation(s)
- Julian Jacob
- Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, Department of Radiation Oncology, France; Sorbonne Université, CNRS, Service de Santé des Armées, Cognition and Action Group, Paris, France.
| | - Thomas Durand
- Sorbonne Université, CNRS, Service de Santé des Armées, Cognition and Action Group, Paris, France; Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, Department of Neurology, France
| | - Loïc Feuvret
- Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, Department of Radiation Oncology, France
| | - Jean-Jacques Mazeron
- Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, Department of Radiation Oncology, France
| | - Jean-Yves Delattre
- Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, Department of Neurology, France; Sorbonne Université, INSERM, CNRS, Assistance Publique-Hôpitaux de Paris, Institut du Cerveau et de la Moelle épinière, France
| | - Khê Hoang-Xuan
- Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, Department of Neurology, France; Sorbonne Université, INSERM, CNRS, Assistance Publique-Hôpitaux de Paris, Institut du Cerveau et de la Moelle épinière, France
| | - Dimitri Psimaras
- Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, Department of Neurology, France; Sorbonne Université, INSERM, CNRS, Assistance Publique-Hôpitaux de Paris, Institut du Cerveau et de la Moelle épinière, France
| | - Hassen Douzane
- Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, Department of Neurology, France
| | - Monica Ribeiro
- Sorbonne Université, CNRS, Service de Santé des Armées, Cognition and Action Group, Paris, France; Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, Department of Neurology, France
| | - Laurent Capelle
- Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, Department of Neurosurgery, France
| | - Alexandre Carpentier
- Sorbonne Université, INSERM, CNRS, Assistance Publique-Hôpitaux de Paris, Institut du Cerveau et de la Moelle épinière, France; Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, Department of Neurosurgery, France
| | - Damien Ricard
- Sorbonne Université, CNRS, Service de Santé des Armées, Cognition and Action Group, Paris, France; Service de Santé des Armées, Hôpital d'Instruction des Armées Percy, Department of Neurology, Clamart, France; Service de Santé des Armées, Ecole du Val-de-Grâce, Paris, France
| | - Philippe Maingon
- Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, Department of Radiation Oncology, France
| |
Collapse
|
43
|
Jarrett AM, Hormuth DA, Barnes SL, Feng X, Huang W, Yankeelov TE. Incorporating drug delivery into an imaging-driven, mechanics-coupled reaction diffusion model for predicting the response of breast cancer to neoadjuvant chemotherapy: theory and preliminary clinical results. Phys Med Biol 2018; 63:105015. [PMID: 29697054 PMCID: PMC5985823 DOI: 10.1088/1361-6560/aac040] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Clinical methods for assessing tumor response to therapy are largely rudimentary, monitoring only temporal changes in tumor size. Our goal is to predict the response of breast tumors to therapy using a mathematical model that utilizes magnetic resonance imaging (MRI) data obtained non-invasively from individual patients. We extended a previously established, mechanically coupled, reaction-diffusion model for predicting tumor response initialized with patient-specific diffusion weighted MRI (DW-MRI) data by including the effects of chemotherapy drug delivery, which is estimated using dynamic contrast-enhanced (DCE-) MRI data. The extended, drug incorporated, model is initialized using patient-specific DW-MRI and DCE-MRI data. Data sets from five breast cancer patients were used-obtained before, after one cycle, and at mid-point of neoadjuvant chemotherapy. The DCE-MRI data was used to estimate spatiotemporal variations in tumor perfusion with the extended Kety-Tofts model. The physiological parameters derived from DCE-MRI were used to model changes in delivery of therapy drugs within the tumor for incorporation in the extended model. We simulated the original model and the extended model in both 2D and 3D and compare the results for this five-patient cohort. Preliminary results show reductions in the error of model predicted tumor cellularity and size compared to the experimentally-measured results for the third MRI scan when therapy was incorporated. Comparing the two models for agreement between the predicted total cellularity and the calculated total cellularity (from the DW-MRI data) reveals an increased concordance correlation coefficient from 0.81 to 0.98 for the 2D analysis and 0.85 to 0.99 for the 3D analysis (p < 0.01 for each) when the extended model was used in place of the original model. This study demonstrates the plausibility of using DCE-MRI data as a means to estimate drug delivery on a patient-specific basis in predictive models and represents a step toward the goal of achieving individualized prediction of tumor response to therapy.
Collapse
Affiliation(s)
- Angela M. Jarrett
- Institute for Computational Engineering and Sciences, The University of Texas at Austin Austin, Texas USA
| | - David A. Hormuth
- Institute for Computational Engineering and Sciences, The University of Texas at Austin Austin, Texas USA
| | - Stephane L. Barnes
- Institute for Computational Engineering and Sciences, The University of Texas at Austin Austin, Texas USA
| | - Xinzeng Feng
- Institute for Computational Engineering and Sciences, The University of Texas at Austin Austin, Texas USA
| | - Wei Huang
- Advanced Imaging Research Center Oregon Health and Science University Portland, Oregon USA
| | - Thomas E. Yankeelov
- Institute for Computational Engineering and Sciences, The University of Texas at Austin Austin, Texas USA
- Livestrong Cancer Institutes, The University of Texas at Austin Austin, Texas USA
- Department of Biomedical Engineering, The University of Texas at Austin Austin, Texas USA
- Department of Oncology, The University of Texas at Austin Austin, Texas USA
- Department of Diagnostic Medicine, The University of Texas at Austin Austin, Texas USA
| |
Collapse
|
44
|
Hormuth DA, Weis JA, Barnes SL, Miga MI, Quaranta V, Yankeelov TE. Biophysical Modeling of In Vivo Glioma Response After Whole-Brain Radiation Therapy in a Murine Model of Brain Cancer. Int J Radiat Oncol Biol Phys 2018; 100:1270-1279. [PMID: 29398129 PMCID: PMC5934308 DOI: 10.1016/j.ijrobp.2017.12.004] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 10/17/2017] [Accepted: 12/03/2017] [Indexed: 02/05/2023]
Abstract
PURPOSE To develop and investigate a set of biophysical models based on a mechanically coupled reaction-diffusion model of the spatiotemporal evolution of tumor growth after radiation therapy. METHODS AND MATERIALS Post-radiation therapy response is modeled using a cell death model (Md), a reduced proliferation rate model (Mp), and cell death and reduced proliferation model (Mdp). To evaluate each model, rats (n = 12) with C6 gliomas were imaged with diffusion-weighted magnetic resonance imaging (MRI) and contrast-enhanced MRI at 7 time points over 2 weeks. Rats received either 20 or 40 Gy between the third and fourth imaging time point. Diffusion-weighted MRI was used to estimate tumor cell number within enhancing regions in contrast-enhanced MRI data. Each model was fit to the spatiotemporal evolution of tumor cell number from time point 1 to time point 5 to estimate model parameters. The estimated model parameters were then used to predict tumor growth at the final 2 imaging time points. The model prediction was evaluated by calculating the error in tumor volume estimates, average surface distance, and voxel-based cell number. RESULTS For both the rats treated with either 20 or 40 Gy, significantly lower error in tumor volume, average surface distance, and voxel-based cell number was observed for the Mdp and Mp models compared with the Md model. The Mdp model fit, however, had significantly lower sum squared error compared with the Mp and Md models. CONCLUSIONS The results of this study indicate that for both doses, the Mp and Mdp models result in accurate predictions of tumor growth, whereas the Md model poorly describes response to radiation therapy.
Collapse
Affiliation(s)
- David A Hormuth
- Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, Texas.
| | - Jared A Weis
- Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, North Carolina; Comprehensive Cancer Center, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina
| | - Stephanie L Barnes
- Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, Texas
| | - Michael I Miga
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee; Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee; Department of Neurological Surgery, Vanderbilt University, Nashville, Tennessee
| | - Vito Quaranta
- Department of Cancer Biology, Vanderbilt University, Nashville, Tennessee
| | - Thomas E Yankeelov
- Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, Texas; Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas; Department of Internal Medicine, The University of Texas at Austin, Austin, Texas
| |
Collapse
|
45
|
Hormuth DA, Eldridge SL, Weis JA, Miga MI, Yankeelov TE. Mechanically Coupled Reaction-Diffusion Model to Predict Glioma Growth: Methodological Details. Methods Mol Biol 2018; 1711:225-241. [PMID: 29344892 DOI: 10.1007/978-1-4939-7493-1_11] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Biophysical models designed to predict the growth and response of tumors to treatment have the potential to become a valuable tool for clinicians in care of cancer patients. Specifically, individualized tumor forecasts could be used to predict response or resistance early in the course of treatment, thereby providing an opportunity for treatment selection or adaption. This chapter discusses an experimental and modeling framework in which noninvasive imaging data is used to initialize and parameterize a subject-specific model of tumor growth. This modeling approach is applied to an analysis of murine models of glioma growth.
Collapse
Affiliation(s)
- David A Hormuth
- Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, TX, USA.
| | - Stephanie L Eldridge
- Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, TX, USA.,Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Jared A Weis
- Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC, USA.,Comprehensive Cancer Center, Wake Forest Baptist Medical Center, Winston-Salem, NC, USA
| | - Michael I Miga
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.,Department of Radiology, Vanderbilt University, Nashville, TN, USA.,Department of Radiological Sciences, Vanderbilt University, Nashville, TN, USA.,Diagnostic Medicine, The University of Texas at Austin, Austin, TX, USA
| | - Thomas E Yankeelov
- Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, TX, USA. .,Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA. .,Diagnostic Medicine, The University of Texas at Austin, Austin, TX, USA. .,Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, USA.
| |
Collapse
|
46
|
Neufeld Z, von Witt W, Lakatos D, Wang J, Hegedus B, Czirok A. The role of Allee effect in modelling post resection recurrence of glioblastoma. PLoS Comput Biol 2017; 13:e1005818. [PMID: 29149169 PMCID: PMC5711030 DOI: 10.1371/journal.pcbi.1005818] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Revised: 12/01/2017] [Accepted: 10/09/2017] [Indexed: 11/24/2022] Open
Abstract
Resection of the bulk of a tumour often cannot eliminate all cancer cells, due to their infiltration into the surrounding healthy tissue. This may lead to recurrence of the tumour at a later time. We use a reaction-diffusion equation based model of tumour growth to investigate how the invasion front is delayed by resection, and how this depends on the density and behaviour of the remaining cancer cells. We show that the delay time is highly sensitive to qualitative details of the proliferation dynamics of the cancer cell population. The typically assumed logistic type proliferation leads to unrealistic results, predicting immediate recurrence. We find that in glioblastoma cell cultures the cell proliferation rate is an increasing function of the density at small cell densities. Our analysis suggests that cooperative behaviour of cancer cells, analogous to the Allee effect in ecology, can play a critical role in determining the time until tumour recurrence. Mathematical models of propagating fronts have been used to represent a wide variety of biological phenomena from action potentials in neural cells to invasive species in ecology and epidemic spreading. Here we show that when such models are used to predict the effects of external perturbations the results can be very sensitive to certain details of the local dynamics. For example, the post resection recurrence of tumour growth depends strongly on the density dependence of the proliferation of cancer cells. This suggests that targeting the cooperative behaviour of cancer cells could be an efficient strategy for delaying the recurrence of diffuse aggressive brain tumours.
Collapse
Affiliation(s)
- Zoltan Neufeld
- School of Mathematics and Physics, The University of Queensland, St. Lucia, Brisbane, Queensland, Australia
- * E-mail:
| | - William von Witt
- School of Mathematics and Physics, The University of Queensland, St. Lucia, Brisbane, Queensland, Australia
| | - Dora Lakatos
- Department of Biological Physics, Eotvos University, Budapest, Hungary
| | - Jiaming Wang
- School of Gifted Young, University of Science and Technology of China, Hefei, China
| | - Balazs Hegedus
- Department of Thoracic Surgery, Ruhrlandklinik, University Duisburg-Essen, Essen, Germany
- MTA-SE Molecular Oncology Research Group, Hungarian Academy of Sciences - Semmelweis University, Budapest, Hungary
| | - Andras Czirok
- Department of Biological Physics, Eotvos University, Budapest, Hungary
- Department of Anatomy and Cell Biology, University of Kansas Medical Center, Kansas City, Kansas, United States of America
| |
Collapse
|
47
|
Protopapa M, Zygogianni A, Stamatakos GS, Antypas C, Armpilia C, Uzunoglu NK, Kouloulias V. Clinical implications of in silico mathematical modeling for glioblastoma: a critical review. J Neurooncol 2017; 136:1-11. [PMID: 29081039 DOI: 10.1007/s11060-017-2650-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2017] [Accepted: 10/22/2017] [Indexed: 01/22/2023]
Abstract
Glioblastoma remains a clinical challenge in spite of years of extensive research. Novel approaches are needed in order to integrate the existing knowledge. This is the potential role of mathematical oncology. This paper reviews mathematical models on glioblastoma from the clinical doctor's point of view, with focus on 3D modeling approaches of radiation response of in vivo glioblastomas based on contemporary imaging techniques. As these models aim to provide a clinically useful tool in the era of personalized medicine, the integration of the latest advances in molecular and imaging science and in clinical practice by the in silico models is crucial for their clinical relevance. Our aim is to indicate areas of GBM research that have not yet been addressed by in silico models and to point out evidence that has come up from in silico experiments, which may be worth considering in the clinic. This review examines how close these models have come in predicting the outcome of treatment protocols and in shaping the future of radiotherapy treatments.
Collapse
Affiliation(s)
- Maria Protopapa
- Radiation Oncology Unit, 1st Department of Radiology, Aretaieio University Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Anna Zygogianni
- Radiation Oncology Unit, 1st Department of Radiology, Aretaieio University Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Georgios S Stamatakos
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Christos Antypas
- Radiation Oncology Unit, 1st Department of Radiology, Aretaieio University Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Christina Armpilia
- Radiation Oncology Unit, 1st Department of Radiology, Aretaieio University Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Nikolaos K Uzunoglu
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Vassilis Kouloulias
- Radiation Oncology Unit, 2nd Department of Radiology, Attikon University General Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece. .,Radiotherapy Unit, 2nd Department of Radiology, ATTIKON University Hospital, National and Kapodistrian University of Athens, Medical School, Athens, Greece.
| |
Collapse
|
48
|
Elazab A. Low grade glioma growth modeling considering chemotherapy and radiotherapy effects from magnetic resonance images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:3077-3080. [PMID: 29060548 DOI: 10.1109/embc.2017.8037507] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Studying tumor growth using mathematical models from magnetic resonance (MR) images is an important application that is believed to play a major role in cancer treatment by predicting tumor evolution, quantifying the response to therapy, and treatment planning. Reaction diffusion is the most popular model because of its simplicity and consistency with the biological growth process. However, most of the current growth models focus on presurgical images and likely without treatment. In this paper, we propose a new reaction diffusion model to consider the chemotherapy and radiotherapy effects on the tumor growth modelling for patients with low grade glioma. The proposed model does not consider the tensor information from diffusion tensor imaging. Instead it uses a weighted parameter to promote higher diffusivity in white matter. The radiotherapy and chemotherapy effects are considered as a loss terms in the proposed model. The preliminary results of the proposed model on synthetic and 2 real MR images show that, our model can effectively simulate tumor growth with high accuracies when treatments are administrated to low grade glioma patients.
Collapse
|
49
|
A Patient-Specific Anisotropic Diffusion Model for Brain Tumour Spread. Bull Math Biol 2017; 80:1259-1291. [DOI: 10.1007/s11538-017-0271-8] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Accepted: 03/15/2017] [Indexed: 02/01/2023]
|
50
|
Patel V, Hathout L. Image-driven modeling of the proliferation and necrosis of glioblastoma multiforme. Theor Biol Med Model 2017; 14:10. [PMID: 28464925 PMCID: PMC5414170 DOI: 10.1186/s12976-017-0056-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Accepted: 04/20/2017] [Indexed: 12/03/2022] Open
Abstract
Background The heterogeneity of response to treatment in patients with glioblastoma multiforme suggests that the optimal therapeutic approach incorporates an individualized assessment of expected lesion progression. In this work, we develop a novel computational model for the proliferation and necrosis of glioblastoma multiforme. Methods The model parameters are selected based on the magnetic resonance imaging features of each tumor, and the proposed technique accounts for intrinsic cell division, tumor cell migration along white matter tracts, as well as central tumor necrosis. As a validation of this approach, tumor growth is simulated in the brain of a healthy adult volunteer using parameters derived from the imaging of a patient with glioblastoma multiforme. A mutual information metric is calculated between the simulated tumor profile and observed tumor. Results The tumor progression profile generated by the proposed model is compared with those produced by existing models and with the actual observed tumor progression. Both qualitative and quantitative analyses show that the model introduced in this work replicates the observed progression of glioblastoma more accurately relative to prior techniques. Conclusions This image-driven model generates improved tumor progression profiles and may contribute to the development of more reliable prognostic estimates in patients with glioblastoma multiforme.
Collapse
Affiliation(s)
- Vishal Patel
- Department of Radiological Sciences Ronald Reagan-UCLA Medical Center, University of California, Los Angeles, 757 Westwood Plaza, Suite 1638, Los Angeles, 90095, CA, USA.
| | - Leith Hathout
- Harvard Medical School, 25 Shattuck Street, Boston, 02115, MA, USA
| |
Collapse
|